{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-11-21T08:24:24.989092Z",
     "start_time": "2025-11-21T08:24:24.076606Z"
    }
   },
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from streamlit import title\n",
    "from sympy import rotations\n",
    "from wordcloud import WordCloud, ImageColorGenerator\n",
    "online=pd.read_csv('online_retail_II.csv')\n",
    "online.head(10)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "5  489434     22064           PINK DOUGHNUT TRINKET POT         24   \n",
       "6  489434     21871                  SAVE THE PLANET MUG        24   \n",
       "7  489434     21523   FANCY FONT HOME SWEET HOME DOORMAT        10   \n",
       "8  489435     22350                            CAT BOWL         12   \n",
       "9  489435     22349       DOG BOWL , CHASING BALL DESIGN        12   \n",
       "\n",
       "           InvoiceDate  Price  Customer ID         Country  \n",
       "0  2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
       "1  2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "2  2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "3  2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
       "4  2009-12-01 07:45:00   1.25      13085.0  United Kingdom  \n",
       "5  2009-12-01 07:45:00   1.65      13085.0  United Kingdom  \n",
       "6  2009-12-01 07:45:00   1.25      13085.0  United Kingdom  \n",
       "7  2009-12-01 07:45:00   5.95      13085.0  United Kingdom  \n",
       "8  2009-12-01 07:46:00   2.55      13085.0  United Kingdom  \n",
       "9  2009-12-01 07:46:00   3.75      13085.0  United Kingdom  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>489434</td>\n",
       "      <td>22064</td>\n",
       "      <td>PINK DOUGHNUT TRINKET POT</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>489434</td>\n",
       "      <td>21871</td>\n",
       "      <td>SAVE THE PLANET MUG</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>489434</td>\n",
       "      <td>21523</td>\n",
       "      <td>FANCY FONT HOME SWEET HOME DOORMAT</td>\n",
       "      <td>10</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>5.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>489435</td>\n",
       "      <td>22350</td>\n",
       "      <td>CAT BOWL</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:46:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>489435</td>\n",
       "      <td>22349</td>\n",
       "      <td>DOG BOWL , CHASING BALL DESIGN</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:46:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:27:55.781118Z",
     "start_time": "2025-11-21T08:27:55.528201Z"
    }
   },
   "cell_type": "code",
   "source": [
    "online.shape\n",
    "online.isnull().sum()\n",
    "online.info()"
   ],
   "id": "69b6f23250d404c7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1067371 entries, 0 to 1067370\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count    Dtype  \n",
      "---  ------       --------------    -----  \n",
      " 0   Invoice      1067371 non-null  object \n",
      " 1   StockCode    1067371 non-null  object \n",
      " 2   Description  1062989 non-null  object \n",
      " 3   Quantity     1067371 non-null  int64  \n",
      " 4   InvoiceDate  1067371 non-null  object \n",
      " 5   Price        1067371 non-null  float64\n",
      " 6   Customer ID  824364 non-null   float64\n",
      " 7   Country      1067371 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 65.1+ MB\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:33:42.777686Z",
     "start_time": "2025-11-21T08:33:42.646780Z"
    }
   },
   "cell_type": "code",
   "source": "display(online.isnull().sum())",
   "id": "380c268e4aa7e807",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Invoice             0\n",
       "StockCode           0\n",
       "Description      4382\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "Price               0\n",
       "Customer ID    243007\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:54:26.000425Z",
     "start_time": "2025-11-21T08:54:25.764989Z"
    }
   },
   "cell_type": "code",
   "source": [
    "online=online.dropna(subset=['Customer ID'])\n",
    "online=online[online[\"Quantity\"]>0&(online[\"Price\"]>0)]\n",
    "online[\"InvoiceDate\"]=pd.to_datetime(online[\"InvoiceDate\"])\n",
    "online[\"Customer ID\"]=online[\"Customer ID\"].astype(int)\n",
    "online[\"TotaSales\"]=online[\"Quantity\"]*online[\"Price\"]\n",
    "print(\"Data clean\")\n"
   ],
   "id": "bb1bddf530b31daa",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data clean\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T09:00:29.780928Z",
     "start_time": "2025-11-21T09:00:29.079509Z"
    }
   },
   "cell_type": "code",
   "source": [
    "top=online[\"Country\"].value_counts().head(20)\n",
    "sns.barplot(y=top.index,x=top.values,palette=\"viridis\")\n",
    "plt.title(\"Top 10 Most Sold Products\")\n",
    "plt.xlabel(\"Quantity Sold\")\n",
    "plt.ylabel(\"Product description\")\n",
    "plt.show()\n"
   ],
   "id": "ed8299c614363cab",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\a2719\\AppData\\Local\\Temp\\ipykernel_12144\\2072192317.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(y=top.index,x=top.values,palette=\"viridis\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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7Z3Tu3NmoWLGi8cYbbxhTpkwxZs2aleTvalBQkNGkSROjfPnyhpeXlzFjxgwjPj7eMAzDiI2NNTp16mSUL1/e6Nq1q2EYhvHbb78ZnTt3NqpVq2ZUqFDB8PX1NX766aenHrNIWrEyDL09W0ReTMHBwbz77rssWbKE6tWrp3c4IiIvJF0zKCIiIpKFqRgUERERycJ0mlhEREQkC9PKoIiIiEgWpmJQREREJAtTMSgiIiKShemh0/JEZrOZe/fuYW1tbXk9k4iIiGRshmFgNpuxtbV96puJVAzKE927d4/jx4+ndxgiIiLyHzx4neWTqBiUJ3rwr4ly5col+12smZHJZOLMmTOUKVMGGxub9A4nXSgHygEoB6AcgHIAGT8HJpOJ48ePJ+t95Xq0jDyRyWQiJCQENze3DPmXXURE5EVmMpmxsUn9WzhS8v2tlUFJlikD53Puz7D0DkNERCTTKF66MIOn90rvMDJXMRgTE0NCQgJ58+ZN71AynfD/XeLvP86ndxgiIiKSytLt0TJeXl4EBQUlaQ8KCsLLyytZc6xfv54mTZpYPnt7e3PmzJn/HJOrqyvBwcGP7HtUvLt27cLd3Z0pU6YA0KRJE9avX/+f9/8kgwcPZvDgwWkyt4iIiGRdL/TKYPPmzWnevLnlc3R09HPb97p16wgICGDo0KG0bdsWgE2bNj23/YuIiIikhgz90Onw8HBcXV1ZvXo1Xl5eVKlShffff5/Lly8DiVcRfXx8AOjatSvz5s0DYN++ffj5+eHh4ZFk1S4hIYFx48ZRvXp1atSowfz585Md14IFCxg5ciTTp0+3FIKQePXQ39+fyZMn06FDB9zd3XnrrbfYvHlzomPr0qULlStX5s0332Tx4sW4urpa+rdv306TJk1wc3Oje/fuSQrd1atX06RJEypXrkyzZs0SHZu/vz/Tp0/nnXfewc3NjebNm/P7778zYMAAKleujJeXFzt37kz28YqIiEjmlaGLwQd27tzJ2rVr2bp1K1evXmXWrFlJxmzduhWAefPm0bVrV06dOkXPnj3p1q0bwcHBjB49mrFjx7J7924AZs2axc6dO1mzZg07duzg9OnTT43DMAwmTJjApEmTmD9/PnXr1n3i+FWrVjF06FCCg4Np1KgRAQEB3L17F5PJRPfu3SlQoAB79uxhwYIFrF271rLd//73P/r160f37t05fPgwbdq0scQN94vg8ePHM2zYMA4dOsSQIUMYOXIkP//8s2XMypUrGT16NAcPHiRnzpy0b9+et956i+DgYHx8fBg9evRTj1dEREQyvxeiGOzatSs5c+Ykf/78eHl5cf78+adus2LFCho0aECjRo2wsbGhcuXKtG3blmXLlgH3T/N26dKFYsWKkT17doYNG/bUN2zMmDGDPXv2UKRIEVauXPnUGHx8fCzP52vVqhUxMTFcu3aNkJAQzp8/z+eff0727NkpUqQI/fv3t2y3efNmXn/9dZo3b46trS0NGzakfv36lv7vv/+edu3aUbNmTWxsbKhZsybt2rVjxYoVifZdunRp7O3t8fDwoGTJkjRs2BA7Ozvq1KlDRETEU+MXERGRzC/drhm0t7fHZDIlaTeZTEkebpw/f37Lz7a2tiTn0YgREREcOHAADw+PRHMXL14cgKioKAoVKmTpy5kzJ7ly5XrinEWLFmXWrFmEhobyzjvv8Nprr9GpU6fHjnd2dk4UN9x/vdvly5fJkycP2bNnTzT3A5GRkRQuXDjRXMWLF7ecKr569SrFihVLEtuOHTssn3Pnzm352cbGJtGxWVtbJyuHIiIikvmlWzFYqFChR65OXbhwgSJFijzz/C4uLrRq1YpRo0ZZ2qKioixFkIuLC2Fh//fcvNu3bxMTE/PEOVu1asVLL71E+fLl+fzzzxk+fDhly5bljTfeSFFshQsX5vr169y5c4ds2bIBcPHixUSx//uavsuXL+Pg4ADcL/xCQ0MT9YeFhSUqPvUeYREREUmOdDtN3KJFC7777jv27t2L2WwmPj6eXbt2sXr1anx9ff/TnPb29paCzs/Pj40bN7Jnzx7MZjPnz5+nY8eOLFy4EIA2bdowf/58zp49y927dxk/fvwjVyofp02bNrRs2ZL+/fsnKiqTo1KlSpQuXZrx48dz584dIiMjmT59uqW/efPmnD59mlWrVnHv3j327NmT6HpAPz8/Vq5cyf79+zGZTBw4cICVK1fSunXrFMUhIiIikm4rgy1btiQhIYEvv/yS0NBQzGYzr7zyCkOGDEn07MCUaNeuHQMGDKBTp07079+fKVOmMGXKFPr160e2bNlo2rQpH3/8MXD/OsQ7d+7QsWNH7t27R9u2bROdWk2O4cOH8+eff9KrV69E1+s9jbW1NdOnT2f48OHUrFkTFxcXvLy8+PPPPwEoVqwYs2fPZvz48XzxxReUL18eb29vy/ZvvfUWsbGxjBkzhosXL1KwYEEGDhxIy5YtUxS/iIiIiN5NnA7i4uI4duwY1apVs7wvcMeOHQwfPjzRXcMZwYN3Gy4Zs4E/j/yd3uGIiIhkGqVff5lZm8ekydx6N3EGZ2dnx0cffcRHH31Eu3btiI6OZuHChYnuGM5oipYsRMLde+kdhoiISKZRvHThpw96DrQymE4OHz7MxIkTOXv2LA4ODvj4+PDpp58musM4I0jJvyxEREQkZUwmMzY2qX8LR0q+v1+I5wxmRh4eHqxatYojR46wb98+hg8fnuEKwYel5OaazMhkMnHy5MksnQflQDkA5QCUA1AOIPVykBaFYEqlfwQiL4g7d+6kdwjpTjlQDkA5AOUAlAPIPDlQMSgiIiKShakYlGRJ7vWCZpM5jSMRERGR1KS7iSVZvhq+jPNnLj5xTLFXXBg4vtPzCUhERERShYpBSZaIC5Gc/TNlb1oRERGRjE+niUVERESyMK0MJsPFixeZM2cOu3fv5vr169jb21OhQgU6d+6Mp6dneocnIiIi8p9pZfApTp8+TfPmzYmPj2fevHkcOXKEn376iebNm/Phhx/y66+/pneIIiIiIv+ZisGnCAgIwNPTk3HjxlGqVClsbGzInTs3LVq0YPjw4SQkJACwb98+/Pz88PDwoEmTJqxfv94yx+DBg+nbty9vvfUWNWrUIDQ0FFdXV1auXImPjw+VKlWiR48e/PHHH7z99tu4u7vTunVrLly4AEB8fDwTJkzgrbfewt3dnZo1azJ69GgevDzG39+fyZMn06FDB9zd3XnrrbfYvHkzAHPnzsXHxyfRMS1YsIAOHTo8j/SJiIhIBqdi8AkuX77MsWPHePvttx/Z36pVKxo2bMipU6fo2bMn3bp1Izg4mNGjRzN27Fh2795tGbt7926mTZvGTz/9RPHixQHYsGEDK1eu5Oeff+bIkSP06tWLL774gr1792Jvb8/s2bMB+Oabb9i9ezfffPMNx44dY9asWaxYsYIDBw5Y5l+1ahVDhw4lODiYRo0aERAQwN27d2nZsiVhYWH89ttvlrFr167F19c3LVImIiIiLxgVg09w+fJlAFxcXCxt+/fvx8PDAw8PD9zd3fHx8WHFihU0aNCARo0aYWNjQ+XKlWnbti3Lli2zbOfm5kbZsmXJmTOnpa1jx47kzp2bAgUKUKZMGRo1akSpUqXInj07NWrUICIiAoC2bduyePFinJ2diYqKIi4ujhw5chAZGWmZy8fHh3LlymFvb0+rVq2IiYnh2rVrFChQgNq1a7Nu3ToATpw4QXh4OG+++Waa5k5EREReDLqB5AmcnZ0BiIyM5JVXXgGgZs2aHD58GICgoCBmzJhBREQEBw4cwMPDw7KtyWSyrAACFChQIMn8uXPntvxsY2NDrly5LJ+tra0tp4Hv3LnDqFGjOHToEC4uLpQrVw7DMDCb/+8Bzw9iBbC1vf9rfdDv6+vL8OHD+eyzz/jhhx948803yZEjx39LioiIiGQqKgafoEiRIlSoUIHVq1dTo0aNx45zcXGhVatWjBo1ytIWFRVlKeYArKyskmz3qLZHGTZsGLly5WLPnj04ODhgNpupWrVqso/Dy8uL4cOHs3fvXrZs2cK0adOSva2IiIhkbjpN/BQPrv37/PPPOXfuHIZhEBsby9q1awkMDKRAgQL4+fmxceNG9uzZg9ls5vz583Ts2JGFCxemSgyxsbE4ODhgbW1NbGwsEydOJDY21nLzytPY2dnRvHlzpk2bhpOTU6IVTBEREcnaVAw+RdmyZdm4cSOOjo706NGDKlWqULduXVatWsUHH3zAkiVLqFSpElOmTGHKlClUrVqVjh074uXlxYABA1IlhmHDhnHq1CmqVavGm2++SWxsLLVr1+b06dPJnsPX15eTJ0/qxhERERFJRKeJk6FAgQIMHTqUoUOHPnZMvXr1qFev3iP7xo8fn6Ttr7/+SvR56dKliT736dPH8vPrr79OUFDQY/f9722LFi2aZH4XFxfs7e1p2bLlY+cRERGRrEfFYCYXHx/PhQsXWLJkCXXr1qVgwYL/aZ4iJQqSEG964phir7g8sV9EREQyHhWDmVx8fDxvv/02hQoVsjy38L/4aGQHbGxsnjrObDJjbaOrD0RERF4UKgYzOScnJ44cOfLM85hMpmQVgyoERUREXiz65hYRERHJwlQMioiIiGRhKgYlWZJzihjuXzMoIiIiLw5dMyjJMn38Gs6fvfzEMcVKFOCT4e88p4hEREQkNagYlGSJCL3C2dMR6R2GiIiIpDIVg+nMy8uLK1euYGub9Fcxb9489u/fz8GDBy0Plv73eMMwsLa25rXXXmPo0KGUK1cOAH9/f44dO4adnV2SeUeOHEnz5s3T8KhERETkRaFiMAMYOXLkY18Tt3///qeOv3r1KsOGDaN3795s27YNa+v7l4J279490ZtMRERERP5NN5BkAvnz56ddu3ZERERw48aN9A5HREREXiAqBjOBS5cu8e2331KhQgXy5s2b3uGIiIjIC0SniTOAkSNHMnbs2ERthQoVYsOGDU8cf+/ePRISEnBxccHb25vu3bsnGjd37ly++eabJNsfPnw49YIXERGRF5qKwQxg+PDhj71m8Enj4+PjWbJkCbNnz6Zu3brkyZMn0bhu3brpmkERERF5Ip0mfoHZ29vzwQcf8M4779CrVy9OnTqV3iGJiIjIC0bFYCbw0Ucf4erqyscff0xcXFx6hyMiIiIvEBWDGcDw4cNxd3dP8mfevHnJ2t7GxoZJkyYRGRnJhAkTLO1z5sx55LyjRo1Kq0MRERGRF4yuGUxnO3bsSJXxxYoV48iRI5bPDx5SLSIiIvIkKgYlWYoUdyYhwfTEMcVKFHhO0YiIiEhqUTEoydJ3sB82NjZPHWc2mbG20dUHIiIiLwp9a0uymExPXhV8QIWgiIjIi0Xf3CIiIiJZmIpBERERkSxMxaAky9OuFzSbzc8pEhEREUlNuoFEkmXaV2s5fz7qkX3Fijnz6UC/5xyRiIiIpAYVg5IsERFXOXv2UnqHISIiIqlMp4lFREREsjCtDKYRLy8vrly5gq1t4hS7u7uzcOHCdIpKREREJDEVg2lo5MiR+Pr6pncYIiIiIo+lYjAd+Pv7U6RIEYKDgzEMg40bN3Lw4EHmzp3LhQsXuH37NhUqVGDMmDG8/PLLBAUFsXr1asqXL8/GjRuxsrLCy8uLESNGYGdnx71795g5cyZBQUHExMTw2muv8fnnn/Pqq68SHx/P119/zfr164mJiaFSpUoMGzaMEiVKpHcaREREJAPQNYPpZN++faxYsYL169cTGxtLv3796NatG/v372fnzp0YhsHMmTMt448ePUq+fPnYvXs3c+bMYfPmzfz0008AfP3112zcuJEFCxZw6NAhqlWrRvfu3TGZTEydOpWdO3eyePFidu/eTaVKlejcuTN3795Nr0MXERGRDEQrg2lo5MiRjB07NlHbrl27AKhTpw4FCxYEwNHRkU2bNlG8eHFiY2O5fPkyefLkITIy0rKdo6MjPXr0wMrKiooVK+Lq6sq5c+cA+OGHH+jevTulS5cGoGfPntStWxez2cyKFSuYPn06xYoVA+DDDz9k1apV7Ny5Ex8fnzTPgYiIiGRsKgbT0PDhwx97zWCBAgUsP9vZ2bFx40ZWrFiBlZUVZcuWJTY2NtHNJ/ny5cPKyirRNoZhAHDlyhUKFy5s6bO3t8fNzY1r165x+/Zt+vXrh7X1/y0CJyQkEBERkWrHKSIiIi8uFYPp5OHCbsuWLXz77bd89913lmv5Ro8ezenTp5M1V6FChbh06f+eAZiQkMCkSZPo0qULDg4OLFy4EDc3N0v///73P8uqpIiIiGRtumYwA4iJicHa2hpHR0cMw2DXrl2sXbuWhISEZG3v6+vLggULOHfuHPfu3WPOnDls27aNvHnz4ufnx+TJk7l8+TJms5kffviBpk2bcuHChTQ+KhEREXkRaGUwA2jVqhVHjhyhSZMm2NjYULJkSd577z2WLVtGfHz8U7f/4IMPuHfvHl26dOHmzZtUqFCBefPmYWdnx6BBgwgMDKR9+/bcuHGDYsWKMX36dMqVK/ccjkxEREQyOhWDaWTHjh2P7Vu6dGmiz/b29kycODHJuL59+wL3V/7+fe3hw3PY2trSu3dvevfunWQOBwcHPvnkEz755JMUxS8iIiJZg4pBSZYiRfKTkGB+ZF+xYs7PORoRERFJLSoGJVn6fdQSGxubx/abzeZEdyyLiIjIi0Hf3pIsJpPpif0qBEVERF5M+gYXERERycJUDEqyPHyK2GR+9LWDIiIi8uLRNYOSLFO+Xs/5C1EUK+rM4H6PfquKiIiIvHhUDEqyRERc5e9zl9M7DBEREUllOk38FFFRUdy+fTu9wxARERFJE5m2GHR1daVbt24YhpGoPSgoCC8vr2TNcfXqVXx8fLh+/ToAgYGB+Pv7p1qMKYklpcLDw3F1dSU8PDxN5hcREZHMIdMWgwC//vor8+fP/8/bx8XFaVVQREREMrVMXQz6+/szbdo0jh49+tgxoaGh9OjRg+rVq1O/fn2mTp1KfHw8JpOJpk2bAtC0aVM2b94MwK1btxg2bBi1atWievXqTJ061TJXfHw806ZNo0GDBlSrVo2uXbty4cIFS7+rqytjxoyhevXq9OjRI0ksa9aswdfXl+rVq+Pu7k737t0TrUr27duXTz75BA8PD+rUqcPkyZMt28bGxjJo0CCqVKlC7dq1WbduXaK5ly9fTsOGDfHw8KBZs2asXr36P2RUREREMptMXQx6e3vTrl07Pv74Y27cuJGk//bt23Tq1IkyZcqwa9culi9fzr59+wgMDMTGxoaNGzcCsHHjRho3bgzAyZMnqVq1Krt372batGnMmTOHY8eOATB16lR27tzJ4sWL2b17N5UqVaJz587cvXvXss/Q0FB27tyZ5F3Ev//+O2PGjGHEiBEEBwezZcsWzp8/z5IlSyxjfvrpJ2rVqkVwcDCjR49m3rx5hISEADBq1CguXLjATz/9xPr16zly5Ihlu7CwMMaNG8fcuXM5fPgwAwcOZPTo0URFRaVKnkVEROTFlamLQYBBgwaRN29eBg8enOT6wZ07dxIfH8/HH3+Mg4MDhQoVol+/fixbtuyx85UpU4YWLVpgZWVFjRo1yJ8/P6GhoRiGwYoVK/j4448pVqwYDg4OfPjhhyQkJLBz507L9k2bNiVbtmzkzJkz0bxly5Zl48aNVKxYkZs3bxIVFUXevHmJjIy0jHn55Zdp2fL+a+Hq1q2Ls7Mz58+fJz4+ni1bttCnTx/y5ctHnjx5GDhwoGU7GxsbS3xHjhyhZs2ahISEUKBAgWfMroiIiLzoMv2jZezt7fnqq69o1aoVCxcuJE+ePJa+iIgIrl+/TtWqVS1thmGQkJDAtWvXHjlf7ty5k8xvMpm4fv06t2/fpl+/folezZaQkEBERITl8+MKMGtra5YsWcKGDRvInj07rq6uxMbGJipgnZ2dE21jZ2eH2WwmOjqa+Ph4ChUqZOkrVqyY5efChQuzdOlS5s+fT48ePTCZTPj6+vLpp5/i4ODwyHhEREQka8j0xSBA8eLFGT16NAMHDsTX9/8emOzi4kLx4sX58ccfLW2xsbFcu3aNvHnzJiriniZPnjw4ODiwcOFC3NzcLO3/+9//KFiwoOWzlZXVI7dfvHgxe/fuZcOGDeTPnx/gkdcVPmnfYWFhlCxZEoDLl//vmYDXrl3DZDIxc+ZMzGYzR48epW/fvrzyyit06NAh2ccoIiIimU+mP038QOPGjWndujUrV660tNWvX59bt24xf/584uPj+eeffxg0aBD9+/fHysrKsmoWGxv71Pmtra3x8/Nj8uTJXL58GbPZzA8//EDTpk0T3UTyOLGxsdja2mJnZ8e9e/dYt24du3fvJiEh4anb2tvb07JlS6ZNm8bly5eJiYlh0qRJlv6LFy/SuXNn9u/fj7W1taU4fXiVVERERLKmLFMMAgwZMoTXXnvN8tnJyYnFixcTHBxMnTp1aNiwIdbW1nz99dcA5M+f33ITynfffffU+QcNGkSlSpVo3749Hh4eLF68mOnTp1OuXLmnbtu5c2cKFSpE/fr1qV27NuvXr6d9+/acPn06Wcc2dOhQKlasSLNmzWjUqBGVKlWy9FWoUIGAgABGjBiBu7s7HTp0oH379rz11lvJmltEREQyLyvj33dViDzEZDIREhLC0jVH+fPMRUq/4sLMSd3TO6zn7kEe3NzcsLGxSe9w0oVyoByAcgDKASgHkPFzkJL4ssQ1g/LsihTJT8I9M8WKOj99sIiIiLwwVAxKsnzcs7nlXxYmsxkb6yx1hYGIiEimpW90SRaTyWT5WYWgiIhI5qFvdREREZEsTMWgiIiISBamYlCS5eHrBUVERCTz0A0kkiyT5m/AZDYY2rNVeociIiIiqUjFoCRL+OVrJNzTqqCIiEhmo9PEIiIiIlmYisF0cvPmTUaMGEHdunVxc3OjVq1aDBo0iMuXLz/z3LNnz+aDDz5IhShFREQks1MxmE769+9PdHQ0a9asISQkhLVr1xIfH8/777/PvXv3nmnuHj16MH/+/FSKVERERDIzFYPp5MiRI3h7e+PsfP/1bvnz52fIkCFUqlSJf/75By8vL2bMmIGPjw/u7u506NCBv//+27L9mjVr8PX1pXr16ri7u9O9e3euX78OQGBgIP7+/gAEBQXxzjvvMGbMGGrUqEHNmjUZOnQoCQkJz/+gRUREJMNRMZhOmjRpwvDhwxkxYgSbN28mIiICZ2dnxo8fT968eQFYuXIlX331Ffv376dUqVL06NGDhIQEfv/9d8aMGcOIESMIDg5my5YtnD9/niVLljxyX0ePHiVfvnzs3r2bOXPmsHnzZn766afnebgiIiKSQakYTCdjxowhICCAS5cuERAQgJeXF97e3qxfv94ypkuXLrz22ms4Ojry2WefcenSJY4ePUrZsmXZuHEjFStW5ObNm0RFRZE3b14iIyMfuS9HR0d69OiBnZ0dFStWxNXVlXPnzj2vQxUREZEMTI+WSSfW1ta0aNGCFi1aYBgGZ8+eZd26dQwcONBy6rhEiRKW8dmyZSN37txcuXIFa2trlixZwoYNG8iePTuurq7ExsZiGMYj95UvXz6srKwsn+3s7B47VkRERLIWrQymg927d+Pu7s6NGzcAsLKyonTp0gwYMIBy5cpx8uRJgEQrfbdu3SI6OppChQqxePFi9u7dy4YNG9i+fTuzZs2iSJEi6XEoIiIi8oJTMZgOqlatSr58+fjss8/466+/SEhIIDY2lvXr13P+/Hnq1asHwKJFi7hw4QJ37txh3LhxlCxZEnd3d2JjY7G1tcXOzo579+6xbt06du/erZtCREREJMV0mjgdODo6snz5cmbMmEHPnj25du0adnZ2uLm5sWjRIkqVKgVAlSpV+PDDD7l48SJVq1Zl7ty5WFtb07lzZ06fPk39+vVxcHCgXLlytG/fngMHDqTzkYmIiMiLRsVgOilQoACjRo164hgPDw/Gjh2bpD137tzMnj37sdv16dPH8rOvry++vr6J+pcuXZrCaEVERCSzUjEoyVLUJR8ms246ERERyWxUDEqyfPpBM2xsbDCZzdhY61JTERGRzELFYAa1Y8eO9A4hEZPJhI2NjQpBERGRTEbf7CIiIiJZmIpBSZYHp4hFREQkc1ExKMmybOs+nSIWERHJhPTtLskSdf1meocgIiIiaUDFoIiIiEgWpmLwP3J1daVixYq4u7vj5uZG1apV6dmzJ5cuXUrW9uHh4bi6uhIeHv7UsYcPH8bd3f1ZQxYRERFJQsXgM5g3bx7Hjh0jJCSEX375BcMw+PTTT1N9Px4eHhw7dizV5xURERFRMZhKnJycaNu2LX/88YelLTY2llGjRlG3bl1q1qxJ//79uXr16iO3Dw8Pp0uXLlSuXJk333yTxYsX4+rqCkBwcLDl50etKAYGBuLv7w9AUFAQ7du3Z8KECVSrVo0aNWqwdOlSVq1aRf369alSpQoBAQFplQYRERF5wagYTCU3b95k06ZNNGrUyNI2ZMgQLly4QFBQENu2bcPJyYnevXtjGIlf62YymejevTsFChRgz549LFiwgLVr1/7nWI4cOULBggU5cOAAffv2Zdy4cQQHB7N582YWL17MmjVrOHTo0H+eX0RERDIPvYHkGfTo0QMbGxvMZjO3bt3ipZdeYs6cOQBcu3aNrVu3smXLFvLlywfcLw49PDw4ceIEuXPntswTEhLC+fPnWb16NdmzZyd79uz079+fbt26/ae4smfPznvvvYeVlRW1atXCZDLRpUsXsmXLRoUKFShQoAARERFUrVr1mXMgIiIiLzYVg89g9uzZVK9eHYC4uDiWLVvGe++9x8qVKzGZTAC0bds20TY2NjaEh4cnKgYvX75Mnjx5yJ49u6WtaNGi/zmu3LlzY2VlBYD1/382YM6cOS391tbWmPUAaREREUHFYKpxdHSkS5cuzJ07l3379tG8eXMAtmzZgrOzs2Xc33//TbFixbhy5YqlrXDhwly/fp07d+6QLVs2AC5evPjI/djY2ACQkJBgaYuOjk405kEhKCIiIvI0umYwldy7d4/vv/+ef/75hypVqlCwYEHq1avHF198QXR0NAkJCXz99df4+fnxzz//JNq2UqVKlC5dmvHjx3Pnzh0iIyOZPn36I/eTL18+cuXKxaZNmzAMgxMnTvDjjz8+j0MUERGRTEgrg8+ga9eulpU6KysrXn75ZaZMmULlypUBmDhxIpMnT6Zly5bExsZSpkwZ5s+fj7Ozc6K7ga2trZk+fTrDhw+nZs2auLi44OXlxZ9//plkn/b29owePZrp06ezYMECXn/9ddq2bcuRI0eez0GLiIhIpqJi8D/666+/njomV65cjBo16pF9RYsWtcwRFxfHpUuXWLhwoaW43LFjBxs2bACgevXqifbn4+ODj4/PI+f19fXF19f3kft5YMeOHU+NXURERLIGnSbOAOzs7Pjoo49YtWoVZrOZa9eusXDhQurXr5/eoVkUyJsrvUMQERGRNKBiMAOwsbFh5syZ/PDDD1StWpVmzZpRpkwZBg8enN6hWXTweQOT7kAWERHJdFJ0mjg4OJiRI0dy/vz5JA9OftT1bZJ8Hh4erFq1Kr3DeCyTyYS9vX16hyEiIiKpLEXF4Pjx46lUqRLDhg3D1laXG4qIiIi86FJU0Z0/f54VK1bg4OCQVvGIiIiIyHOUomsGX375ZaKiotIqFsnAbGxsdM2giIhIJpSilcG33nqLDz74AD8/v0Rv1QBo2bJlasYlGcy32/fxXqPa6R2GiIiIpLIUFYMrVqwA4LvvvkvUbmVlpWIwk7scfTO9QxAREZE0kKJiUA8rTuz8+fO8/PLL6R2GiIiIyH+W4ucM/vHHH4wYMYKuXbsyZMgQDh8+nBZxpaqbN28yYsQI6tati5ubG7Vq1WLQoEFcvnw5RfM0adKE9evXA7Bs2TI+//zztAiXwYMHp9kzBoOCgvDy8kqTuUVEROTFk6JicM+ePbRv354bN27g6upKbGws77//Ptu2bUur+FJF//79iY6OZs2aNYSEhLB27Vri4+N5//33uXfvXrLn2bRpE82bNwfg+vXraRWuiIiIyHOTotPE06dPZ8KECbz11luWti1btjBr1iwaNmyY6sGlliNHjvDFF19YbnrJnz8/Q4YMYfLkyQQEBHDjxg1mzZoFQGBgILNnzyY4OBgnJydOnz5NmzZt2L9/P02bNqV3795YWVkxZ84cTCYTHh4ezJs3j86dOyfa5+3bt2nevDmTJk3i6tWrjB8/nv3792NlZYWXlxcDBw7EycmJ4OBgBg4ciIeHB7/++ivdunVLNE98fDxTp05l586dXL58GUdHRxo3bsywYcOwsrLC398fNzc3jh49ysmTJ3FxcaFPnz40btwYgLNnzzJixAj++OMPihYtSvXq1Z9DxkVERORFkaKVwXPnzuHj45OozcfHh/Pnz6dmTKmuSZMmDB8+nBEjRrB582YiIiJwdnZm/PjxtG7dmv379xMfHw/cX/20tbUlODgYuH+dpKenJ9mzZ7fM16pVK7p3746HhweHDx/G3d2dY8eOWf4MHDiQfPny0bdvX8xmM7169cLa2pqtW7eyYcMGoqKiCAgIsMx3+fJlSpYsyf79+2nfvn2i2L/55ht2797NN998w7Fjx5g1axYrVqzgwIEDljGrVq1i6NChBAcH06hRIwICArh79y4JCQl0796dMmXKcODAAaZMmZLhV3FFRETk+UpRMZg7d25Onz6dqO3UqVNJHjOT0YwZM4aAgAAuXbpEQEAAXl5eeHt7s379etzd3cmePTtHjhzh5s2bnD59mhYtWrB//37gfjHYqFGjZO9r27ZtTJw4ka+//ppixYrxxx9/cOLECYYPH46TkxN58uRh0KBBbNq0iejoaMt2fn5+2NnZ4eTklGi+tm3bsnjxYpydnYmKiiIuLo4cOXIQGRlpGePj40O5cuWwt7enVatWxMTEcO3aNY4dO8alS5cYOHAgDg4OlClThvfff/8ZsykiIiKZSYpOE7dp04aePXvSvXt3ihYtSmhoKPPmzUuympXRWFtb06JFC1q0aIFhGJw9e5Z169YxcOBAnJ2d8fLyYteuXURHR1O5cmXq1KnDlClTuHLlCidPnqR+/frJ2k9ISAiffvopEydOpFKlSgCEh4djMpmoW7duorH29vaEhYVZPhcoUOCRc965c4dRo0Zx6NAhXFxcKFeuHIZhYH7oAdAPF+MPXhNoNpuJjIwkT548ODo6WvqLFy+erGMRERGRrCFFxWDXrl25e/cuc+bM4erVqxQpUoSOHTtm6NWm3bt307dvX3755Rdy586NlZUVpUuXZsCAAezdu5eTJ0/SsGFDJk2aRGxsLLVq1aJGjRqEhoaycuVKqlevTq5cuZ66n3PnztGjRw/69euHt7e3pd3FxQVHR0eCg4OxsbEB7l8HGBYWRokSJThy5Ahw/1mNjzJs2DBy5crFnj17cHBwwGw2U7Vq1WQde6FChbh+/Tq3bt0iR44cACm+g1pEREQytxSdJraysqJPnz788ssvHD9+nB9//JEuXbpgbZ3iJ9Q8N1WrViVfvnx89tln/PXXXyQkJBAbG8v69es5f/489erVo2bNmly6dImff/4ZT09PnJycqFSpEvPnz09U2D3MwcGB2NhYDMPg6tWrfPDBBzRv3pxOnTolGlexYkVKlCjB+PHjuXXrFnFxcYwdO5ZOnTphMpmeGn9sbCwODg5YW1sTGxvLxIkTiY2NJSEh4anburu788orrzBmzBju3LnDhQsXWLhwYbLyJiIiIllDslYG586dS7du3ZgxY8Zjx/Tu3TvVgkpNjo6OLF++nBkzZtCzZ0+uXbuGnZ0dbm5uLFq0iFKlSgFQp04dDh06RNmyZQGoVasWR48efexd0vXr1+e7776jSpUqdO7cmfDwcDZs2MCaNWswDAOAwoULs2nTJubMmcOECRNo1KgRd+/epWLFiixatAgHB4enxj9s2DACAgKoVq0aOXLkoF69etSuXTvJtZuPYmNjw9y5cwkICOCNN94gf/78NGjQgJ9++im56RMREZFMzsp4ULk8QdeuXZk3bx7+/v6PnsTKiiVLlqR6cJL+TCYTISEhbPv7EoPaNU3vcNLNgzy4ublZTvdnNcqBcgDKASgHoBxAxs9BSuJL1srgvHnzAJgyZcoj7xw+c+bMfwhTXiQueZ5+3aSIiIi8eFJ0sd+/nzEI9yvPdu3apVpAkjF1bPAGpofuYBYREZHM4akrgxcuXKBLly4YhsGdO3do0KBBov64uDiKFCmSZgFKxmAymbC3t0/vMERERCSVPbUYLFGiBEOHDiU6OpoRI0YkuVHEwcEh2Y86EREREZGMJVnXDD546HLRokWpVq1amgYkGZONjQ0msxmbDPwYIREREUm5FH2zu7u7M336dBo0aEDFihXx8fFhwYIFaRWbZCBLdu5VISgiIpIJpegNJJMmTWLnzp10796dQoUKERYWxsKFC7l79y69evVKqxglA4i88U96hyAiIiJpIEXF4Pr161m1alWi99vWqFGD9957T8XgM4qKisLJyYns2bOndygiIiKShaTovJ9hGEmeM1i0aFGS8dzqTMXLy4sKFSrg7u6Ou7s7bm5u1KpViwkTJmD+D49fuXr1Kj4+Ply/fj0Nok0sODgYV1fXNN+PiIiIvBhSVAx26NCBYcOG8c8/908Z3r17l/Hjx+Pn55cmwWVkI0eO5NixYxw7doyQkBAWLFjA2rVrn/jKvseJi4vj9u3baRCliIiIyJOl6DTx999/T2RkJFu2bCFXrlzExMRw7949AObMmWMZ9+eff6ZulC8AV1dXqlatysmTJ4mLi2P69Ols2rSJ27dv8+qrr/Lpp59SsWJFy1h/f382bNiAu7s7Bw4cAKBp06aMHTuWs2fPcvDgQZYuXWqZ38vLi969e+Pr60tcXBzjxo1jy5YtZMuWjVatWrF+/XrGjRtH9erVOXr0KF999RX/+9//uHnzJmXKlCEgIAA3N7f0SI2IiIhkYCm+gUSSSkhI4OjRoxw4cIA+ffowYsQITp48yZIlSyhUqBDfffcdnTp1YuPGjRQuXBiA0NBQdu7cSUJCAv/88w8NGjRg48aNFC1alMDAwCfub+zYsfzxxx+sW7eOnDlzMnLkSCIiIoD7q4w9e/akb9++vPPOO8TFxTFkyBAmTpzI8uXL0zwXIiIi8mJJUTGoZwz+n5EjRzJ27FjLZxcXF95//33atGnDhAkTmDlzJiVKlADgvffeY8OGDWzcuJFu3boB91cBs2XLRrZs2Syn3ZMjISGB9evXExgYSKFChQAICAhg48aNANjZ2bFy5UpKlCjB3bt3iYiIIHfu3Bw/fjy1Dl1EREQykWQVg82aNWPDhg14eXlhZWX1yDHbt29P1cAyuuHDh+Pr65ukPSoqioSEBIoWLZqovWjRooSHh1s+FyhQ4D/t98aNG9y5cyfRKwCdnJzIkycPcP/h0MHBwXTt2pXbt29TunRpbG1ts9xNPiIiIpI8ySoGH6xm9enTJ02DyQzy58+Pg4MDYWFhlCpVytIeGhqKl5eX5fPjimoAa2trEhISLJ/NZjM3btwAIF++fDg6OnLx4kVKliwJwO3bt4mOjgbgt99+Y/To0axYsYLXX38dgIULF3Lu3LlUO0YRERHJPJK9Mgjw008/MWnSJJycnNI0qBeZtbU1rVu3ZsqUKbzyyiuWawb//vtvJk+e/MhtHBwcAIiNjQWgVKlSzJ8/nzNnzvDKK6+wcOFCy93G1tbW+Pn5ERgYSJkyZciZMyfjxo3DZDIBEBMTg7W1NY6OjgCEhISwZMkSy40+IiIiIg9L0aNljh07hr29fVrFkmkMHDiQWrVq0alTJ6pXr86WLVtYsGABr7zyyiPH58+fH29vb9q1a8d3331Hw4YNadasGZ06daJ27dpER0dTpUoVy/gBAwZQsmRJGjdujI+PDy4uLlhbW2NnZ4enpyft27enQ4cOVK1alZEjR+Lv78/169e5evXq80qBiIiIvCCsjBRcTDZmzBjCw8Np1qwZzs7OiU51Vq1aNU0ClKQOHTqEq6srOXPmBO6vKFapUoWtW7fy8ssvp+q+TCYTISEh/Hz+EoNbN03VuV8kD/Lg5uaGjY1NeoeTLpQD5QCUA1AOQDmAjJ+DlMSXoruJv/32WwB27tyZqN3KyipLPlswvSxcuNDySBkrKyumT5/OK6+8kuqF4MMK5s6ZZnOLiIhI+klRMXjq1Km0ikNSYMSIEYwcOZK6detiMpmoUqUKc+fOTdN9vlvPE5PZjI11iq4sEBERkQwuRcVgTEwMI0aMoFevXpQqVYpp06YRHh7OiBEjyJEjR1rFKP9SsGBBZs2a9Vz3aTKZdL2oiIhIJpSiZZ7hw4dz8+ZNcufODdx/cHJMTEyihy+LiIiIyIsjRSuD+/btY/v27ZZVwFKlSvHll1/i7e2dJsGJiIiISNpK0cqg2Wy2PM/uAcMwMuRdNJK6bGxsMJnN6R2GiIiIpLIUFYN16tRh0KBBhIaGkpCQQGhoKJ999hm1atVKq/gkg1i8Z69uHhEREcmEUvTtPmTIEGJjY2nUqBEVK1akUaNG3Llzh0GDBqVVfFlKTEwM169fT+8wHiny5s30DkFERETSQIqKwbx587J06VJ27NjBihUr+PXXX1mwYAF58uRJq/gyrGXLluHq6srixYtTbU5vb2/OnDnzn7d3dXUlODgYgCZNmrB+/frUCk1EREQyqRRfM/jzzz9TuHBhXFxcGDduHKNGjbK8UzcrWbZsGe+8806qvvc3Ojo6VeYB2LRpE82bN0+1+URERCRzSlExOH78eMaMGQPcf8zM1atX+d///sfo0aPTJLiMav/+/Vy7do3BgwdjNpvZunWrpc/Ly4ugoCDL5+DgYFxdXS2fAwMDqVu3LtWqVaN169Zs374dAB8fHwC6du3KvHnzCAoKwtfXl86dO+Ph4cGGDRuIjIzko48+wsvLi0qVKtGgQQPWrFnzyBgfjiMl24mIiEjWkqJi8Ndff+W7777j1q1b7Nmzhy+++IIZM2YkeT1dZrd06VLatm2Lo6Mj7du3Z+HChcna7sCBA6xcuZLVq1cTHBxMmzZtGDp0KAkJCZaCct68eXTt2hWAEydO0KxZM/bt24e3tzfDhg3Dzs6OTZs2cfToUTp27Mjo0aO5devWE/f7X7cTERGRzC9FzxmMjo6mcOHC7Ny5kwIFClCiRAlMJlOSx81kZhEREezevZuAgAAA2rZty8yZMzl48CDVqlV74rYODg7cvHmTVatWUb9+fdq0aUO7du2wsrJ65Hg7OztatGiB9f+/i3fMmDHkyJEDOzs7Ll68SI4cOYiLi+PmzZtPfAPMf91OREREMr8UFYPFihVj7dq1/Pjjj9SqVQuz2czChQspXbp0WsWX4Sxfvpx79+7RokULS9u9e/dYuHDhU4tBd3d3AgMDWbp0KfPnz8fR0RF/f3969uxpKfge5uzsnKg9LCyMiRMncv78eV5++WVKlCgB3L+W80n+63YiIiKS+aWoGBw8eDCDBg3C0dGRUaNGceDAARYsWMDs2bPTKr4M5e7du6xZs4YvvviCN954w9J++vRpunXrxtmzZ7G2tiYhIcHS9/BNIRcvXiRfvnwsWLCA+Ph49u/fT+/evSlfvjz16tVLsr+HVwwTEhLo3r07H3/8Me3bt8fKyoo//vjjqXcM/9ftREREJGtIUTFYtWpVduzYYfmcO3dudu3ahb29faoHlhFt2LABKysrmjVrhp2dnaXdxcWFsmXLsnjxYkqVKsX27dtp0aIFMTExLFmyxDLu+PHjBAQE8M033/Dqq6+SL18+AMujeezt7YmJiXnkvhMSEoiLi8PR0RErKysuXrzIpEmTLH2P81+3ExERkawhxa+UOHv2LGPGjKF3797cunWLVatWpUVcGdLy5cuTFIIPtGvXjnXr1tGlSxdu3bqFp6cn7777bqLHu/j4+NC5c2d69uyJm5sb/fr1Y8iQIVSqVMkyx4ABA5g6dWqS+bNnz87YsWOZOXMm7u7uvPvuu3h6epI/f35Onz792Jj/63YiIiKSNVgZhmEkd/DevXvp06cP9evX55dffmHTpk34+vry/vvv061bt7SMU9KJyWQiJCSErREXGdK8WXqHk24e5MHNzS3LvotbOVAOQDkA5QCUA8j4OUhJfClaGZwyZQpTp05l8uTJ2NjYUKhQIebOncvKlSufKWARERERSR8pKgYvXLhAnTp1gP+7uaFChQrc1HtrM72CuXKldwgiIiKSBlJUDBYuXJijR48majt+/DiFChVK1aAk4+lUyxOTHkUjIiKS6aTobuLu3bvTs2dP3nnnHRISEpg3bx5Lly7l448/Tqv4JIMwmUxZ5q5xERGRrCRFxWCTJk1wcnJi2bJlFC5cmAMHDjB06FDLe3VFRERE5MWSomIQoG7dutStWzctYpGM7DGvzBMREZEXW7KKwc8+++ypY8aNG/fMwUjGZfOI1+WJiIjIiy9F3/DR0dGsX7+emJgYcufOzd27d9m4cSPx8fFpFZ+IiIiIpKFkrQw+WPXr0aMH06dPp0GDBpa+PXv2ZJl3E4uIiIhkNilaGQwODqZ+/fqJ2mrWrMmJEydSvONz584xaNAg6tSpg7u7Ow0bNuTLL7/k1q1bljGurq4EBweneO60FhwcjKur62P7vby8CAoKeuo8gwcPZvDgwakZmkVQUBBeXl5pMreIiIhkHikqBosUKcKWLVsStQUFBVGiRIkU7fTo0aO0atWKIkWKsHbtWo4dO8a8efP47bff6Ny5MyaTKUXziYiIiMh/k6K7ifv370+/fv1YtmwZhQoVIjw8nNOnT6f4NHFAQAAtW7akb9++lrZXXnmFqVOnEhAQQFhYGC+//DJw/33IY8eOJTQ0lFKlSjF27FjKli0LwJo1a1i+fDkRERHEx8dTrVo1xo0bR968eQkMDOTMmTPY29uzc+dOsmfPTosWLRgwYAAA/v7+uLm5cfToUU6ePImLiwt9+vShcePGAFy9epXx48ezf/9+rKys8PLyYuDAgTg5OaXoWCMjIxk6dCi///47jo6OVKxYkYCAAAoUKJBoXHx8PFOnTmXnzp1cvnwZR0dHGjduzLBhw7CysnpqvGfPnmXEiBH88ccfFC1alOrVq1vmvnfvHmPGjOHnn3/m3r17lCpVigEDBlClSpUUHYuIiIhkPilaGWzQoAHr16/njTfeIEeOHNStW5f169cnKjyeJjQ0lDNnztC0adMkffnz52fWrFmWQhDg4MGDLFiwgP3795MnTx4mTJgAwO+//86YMWMYMWIEwcHBbNmyhfPnz7NkyRLLtj/99BO1atUiODiY0aNHM2/ePEJCQiz9q1atYujQoQQHB9OoUSMCAgK4e/cuZrOZXr16YW1tzdatW9mwYQNRUVEEBASkJF3A/fc5u7i4sHfvXjZv3szt27eZO3duknHffPMNu3fv5ptvvuHYsWPMmjWLFStWcODAgafGm5CQQPfu3SlTpgwHDhxgypQpbNu2zbLdunXrOHbsGFu2bGHfvn1UrVqVkSNHpvhYREREJPNJ8fNCSpYsSe/evRk1ahS9evWiWLFiKdr++vXrwP3CLznef/998ufPj6OjIw0bNiQ0NBSAsmXLsnHjRipWrMjNmzeJiooib968REZGWrZ9+eWXadmyJTY2NtStWxdnZ2fOnz9v6ffx8aFcuXLY29vTqlUrYmJiuHbtGn/88QcnTpxg+PDhODk5kSdPHgYNGsSmTZuIjo5O0fE6ODhw5MgRNm3axK1bt5g/fz7Dhg1LMq5t27YsXrwYZ2dnoqKiiIuLI0eOHImO53HxHjt2jEuXLjFw4EAcHBwoU6YM77//vmU7R0dHwsPDWbNmDefOnaNfv36sX78+RcchIiIimVOKHzr9rJydnQG4cuVKohXAB65evZqoUMydO7flZzs7O8v1hNbW1ixZsoQNGzaQPXt2XF1diY2NxTCMJPt6eHvzQ+/Xfbjf1vZ+KsxmM+Hh4ZhMpiQP17a3tycsLCxFxzts2DDmzJnDggULGDx4MK+++irDhg3Dw8Mj0bg7d+4watQoDh06hIuLC+XKlcMwjGTFGxkZSZ48eXB0dLT0Fy9e3PJzkyZNSEhIYPXq1UyZMoV8+fLRo0cP3nnnnRQdi4iIiGQ+z70YLFKkCGXLlmXz5s1UrVo1Ud+1a9eoX78+48aNe+Rp5IctXryYvXv3smHDBkvx2KNHj1SJ0cXFBUdHR4KDg7GxsQHuX9MXFhZGiRIlOHLkSLLnOnnyJO3ataNPnz5cv36dmTNn0rt370Snf+F+0ZgrVy727NmDg4MDZrM5SX4ep1ChQly/fp1bt26RI0cOAC5fvmzpP3fuHOXLl6dly5bExcXx448/MmjQIDw8PChTpkyyj0VEREQyn3R5rcTnn3/O999/z4wZM4iOjsYwDP7880969OhB+fLlk/Wu49jYWGxtbbGzs+PevXusW7eO3bt3k5CQ8MzxVaxYkRIlSjB+/Hhu3bpFXFwcY8eOpVOnTim+03n27NmMHj2a2NhYcubMSbZs2ciTJ88jj8fBwQFra2tiY2OZOHEisbGxyToed3d3XnnlFcaMGcOdO3e4cOECCxcutPT/8ssv9O7dm/DwcBwdHcmdOze2tra89NJLKToWERERyXxSVAz27Nnzke0dO3ZM0U6rVavGt99+y8mTJ2nSpAmVK1emb9++1KhRg/nz52NnZ/fUOTp37kyhQoWoX78+tWvXZv369bRv357Tp0+nKJZHsbW1Zc6cOVy9epVGjRpRq1YtQkNDWbRoEQ4ODimaa9SoUZjNZho0aEDVqlX57bffmDZtWpJxw4YN49SpU1SrVo0333yT2NhYateunazjsbGxYe7cuURFRfHGG2/wwQcfJHow+Lvvvku9evV4++23cXNzY9KkSUydOhUXF5cUHYuIiIhkPlbGwxfZPUJ4eDhr164FYM6cOXTv3j1Rf2xsLN9//z2HDh1KsyAl/ZhMJkJCQnBzc7OcMs+KlAflAJQDUA5AOQDlADJ+DlIS31OvGSxcuDBnzpzh+vXrmEymJG8EcXBwYPjw4c8WsYiIiIiki6cWg9bW1pbTmsOGDWPMmDFpHpRkPCazOUP+y0dERESeTYquGRw4cCADBgzg7NmzAEybNo1PP/000fuEJZN68tUEIiIi8oJKUTE4cuRIbt68aXn2X9OmTYmJiWHs2LFpEZuIiIiIpLEUPWdw7969bN++3fIsu1KlSvHll1/i7e2dJsGJiIiISNpK0cqg2WxO8pw9wzB0LVlWYGWV3hGIiIhIGkhRMVinTh0GDRpEaGgoCQkJhIaG8tlnn1GrVq20ik8yCBvrdHk+uYiIiKSxFH3DDxkyhNjYWBo1akTFihXx8fHhzp07DBo0KK3iExEREZE0lKJrBvPmzcvSpUu5ePEiV65cwcXFhYIFC6ZVbPKQqKgonJycyJ49e3qHIiIiIplIilYGDx06xKFDh4iIiCA+Pp7Q0FBLm6Q+f39/AgMDuXr1Kj4+Ply/fj1F24mIiIg8TYpWBv39/ZO0WVtbU6hQIbZv355qQUlicXFx3L59O73DEBERkUwoRSuDp06dSvRn3759vPPOO3To0CGt4svyTCYTTZs2Be4/13Hz5s3Ex8czYcIE3nrrLdzd3alZsyajR4/m36+ZjoyMpFy5chw9etTSdvXqVcqXL09oaOhzPQ4RERHJmJ7pFtG8efPy6aef8s0336RWPPIvNjY2bNy4EYCNGzfSuHFjvvnmG3bv3s0333zDsWPHmDVrFitWrODAgQOJti1YsCCenp6sW7fO0rZ+/Xrc3d0pXrz4cz0OERERyZie+XkhN2/e5O7du6kRiyRT27ZtWbx4Mc7OzkRFRREXF0eOHDmIjIxMMrZ169b8+OOPxMfHA/DDDz/QunXr5x2yiIiIZFApumbws88+S/Q5ISGBI0eO8MYbb6RqUPJkd+7cYdSoURw6dAgXFxfKlSuHYRiYzeYkY728vBg+fDi//vorhQsXJiIiAh8fn3SIWkRERDKiFBWD/+bg4IC/vz/t2rVLrXgkGYYNG0auXLnYs2cPDg4OmM1mqlat+six9vb2NGvWjE2bNlG4cGHeeustPZ5GRERELFJUDI4bNy6t4pAncHBwACA2NtbyvwUKFMDa2prY2FhmzJhBbGwsCQkJj9zez8+PDh06kDdvXsaPH//c4hYREZGML1nF4IwZM546pnfv3s8cjDxa/vz58fb2pl27dgwePJhhw4YREBBAtWrVyJEjB/Xq1aN27dqcPn36kdu/+uqrFC9enNu3b1OlSpXnHL2IiIhkZMkqBoODg4H7z7s7fvw45cqVo2jRokRGRvLbb7/h6emZpkFmVUuXLrX8/O+CPCgoKFnbPVCkSBEqVqyYesGJiIhIppCsYvBBcTF48GB8fX155513LH0//PADP/30U9pEJ88sLCzM8kzIESNGpHc4IiIiksGk6NEyP/30U5KbRZo3b57k+XaSccyYMYPPPvuMIUOGkD9//v88j+kRdyqLiIjIiy9FN5DkzZuXQ4cOUb16dUvbnj17KFCgQKoHJqljwoQJqTPRv95uIiIiIplDiorB7t2707VrV3x8fChcuDBhYWFs27Yt9QoOEREREXmuUlQMtmnThqJFi7J+/Xr++OMPXFxcWLx4MZUrV06r+CSjsLJK7whEREQkDaT4odM1a9akbNmyRERE4OzsTKFChdIiLslgbKyf+c2FIiIikgGlqBiMjY1l0KBB7Nixw9JWs2ZNvvrqK3LmzJnqwYmIiIhI2krRcs/kyZO5desWGzduJCQkhHXr1mE2m5k0aVJaxSciIiIiaShFxeAvv/zC5MmTKVWqFA4ODpQtW5ZJkyaxbdu2tIrvheHq6kq3bt0w/nXXbVBQEF5eXukUlYiIiMiTpagYvHPnDi+99FKitpw5c2LWM+gA+PXXX5k/f356hyEiIiKSbCkqBitVqsS0adMsq1+GYTBt2jQqVKiQJsG9aPz9/Zk2bRpHjx597Ji//vqLrl27Uq1aNerUqcOIESOIiYkB7q8i+vr60rlzZzw8PJgxYwYtWrSwbBsUFISrqytnz54F4J9//uH1118nLCyMyMhIPvroI7y8vKhUqRINGjRgzZo1AMydOxcfH59EcSxYsIAOHTqkdgpERETkBZOiYnDAgAGsWbOGOnXq8Pbbb1OnTh02btzIZ599llbxvVC8vb1p164dH3/8MTdu3EjSHx0dzbvvvkvp0qXZtWsX33//PefOnWPgwIGWMSdOnKBZs2bs27eP1q1b89dffxEZGQncf8C3o6Mje/fuBe6vRJYqVYpixYoxbNgw7Ozs2LRpE0ePHqVjx46MHj2aW7du0bJlS8LCwvjtt98s+1m7di2+vr5pmxARERHJ8FJUDBYrVoytW7daVqAGDx7M5s2bKVWqVFrF98IZNGgQefPmZfDgwUmuH9y+fTt2dnZ88sknODo64uzszOeff86OHTu4cuUKAHZ2drRo0QJ7e3sKFSpE+fLl2b17N2azmX379vH222+zb98+AHbs2IG3tzcAY8aMYfjw4djZ2XHx4kVy5MhBXFwcN2/epECBAtSuXZt169YB9wvO8PBw3nzzzeeYGREREcmIUlQMNm3aFFtbW1q3bk23bt1o0qQJTk5OaRXbC8ne3p6vvvqKQ4cOsXDhwkR9165do3DhwtjY2FjaihYtCkBERAQAzs7OWD/0TD9vb2927drFH3/8Qe7cuWnVqhWHDh0iLi6OXbt2WU7/hoWF0blzZ9544w0GDBjAoUOHACzXc/r6+rJ582YSEhL44YcfePPNN8mRI0faJUJEREReCCl+kvCdO3fSIo5MpXjx4owePZqpU6cSEhJiaS9SpAgXL17EZDJZ2kJDQ4H7RSCA1b/e9NGwYUP27dvHrl27qFWrFq+++irZsmVj/vz55M+fnzJlypCQkED37t1p0aIFwcHBrFq1ivfeey/RPA/uaN67dy9btmyhdevWaXHoIiIi8oJJ0UOnq1evTps2bahTpw4FChRI1Ne7d+9UDexF17hxY4KDg1mxYgVFihQBoG7duowfP54vv/ySfv36ERMTwxdffEGNGjUsY/6tdOnS5MuXj2+//ZZx48YB8MYbbzB//nz8/f0BSEhIIC4uDkdHR6ysrLh48aLl2Y8JCQnA/dPPzZs3Z9q0aTg5OeHh4ZHWKRAREZEXQIpWBsPDwylWrBjnzp0jODjY8ufgwYNpFd8LbciQIbz22muWzy+99BKLFi3i9OnT1K1bl6ZNm1KkSBGmTZv2xHm8vb2JjY2lWrVqANSqVYs7d+5YrhfMnj07Y8eOZebMmbi7u/Puu+/i6elJ/vz5OX36tGUeX19fTp48qRtHRERExMLK+PddDpJp3bhxg9q1a7Nt2zYKFiyYrG1MJhMhISG4ubklutYxq1EelANQDkA5AOUAlAPI+DlISXzJPk08Y8YMTpw4Qa1atfR8uhdMfHw8Fy5cYMmSJdStWzfZhaCIiIhkfsk6TTxx4kSWL1+OnZ0d06dPZ+7cuWkdl6Si+Ph43n77bY4dO8bgwYP/0xwmvWVGREQkU0rWyuDGjRv55ptvKFOmDMHBwYwZM4Zu3bqldWySSpycnDhy5MizTaKrCURERDKlZK0MxsTEUKZMGQCqVKlieSOGiIiIiLzYklUMPvwQZFvbFD2NRkREREQysGQVg7rhWPjXw7BFREQkc0jWMt+9e/dYu3at5XNCQkKizwAtW7ZMxbAko7GxTvHLakREROQFkKxiMH/+/EyfPt3yOU+ePIk+W1lZqRgUEREReQElqxjcsWNHWsfxQrp58yZTp07ll19+4ebNmzg5OeHp6Un//v1xcXFJs/0GBgZy8OBBli5dmmb7EBERkaxB5/6eQf/+/YmOjmbNmjWEhISwdu1a4uPjef/997l37156hyciIiLyVCoGn8GRI0fw9vbG2dkZuH86fciQIVSqVImAgAB69eplGRsYGEj58uWJjY0F4PTp01SqVInbt28TGxvLqFGjqFu3LjVr1qR///5cvXrVsu3Ro0dp3bo1bm5uvP3224SHhyeKY9++ffj5+eHh4UGTJk1Yv369pW/w4MEEBATQo0cP3N3dadCgAUuWLEnLtIiIiMgLRMXgM2jSpAnDhw9nxIgRbN68mYiICJydnRk/fjytW7dm//79xMfHA7Bnzx5sbW0JDg4G7p969/T0JHv27AwZMoQLFy4QFBTEtm3bcHJyonfv3hiGQXR0NN27d8fHx4dDhw7x6aefsm3bNksMp06domfPnnTr1o3g4GBGjx7N2LFj2b17t2VMUFAQ/v7+HDp0iK5duzJ+/Hg9K1JEREQAFYPPZMyYMQQEBHDp0iUCAgLw8vLC29ub9evX4+7uTvbs2Tly5Ag3b97k9OnTtGjRgv379wP3i8FGjRpx7do1tm7dytChQ8mXLx85cuRgyJAhHD9+nBMnTrBz506yZctG165dsbOzo0qVKrRu3doSw4oVK2jQoAGNGjXCxsaGypUr07ZtW5YtW2YZU716dTw9PbG1taV169aYTCZCQ0Ofe75EREQk49ETpJ+BtbU1LVq0oEWLFhiGwdmzZ1m3bh0DBw7E2dkZLy8vdu3aRXR0NJUrV6ZOnTpMmTKFK1eucPLkSerXr8+FCxcAaNu2baK5bWxsCA8PJzIykkKFCmH10HP+ihcvzp9//glAREQEBw4cwMPDw9JvMpkoXry45fOD09gAdnZ2AJj1rmERERFBxeB/tnv3bvr27csvv/xC7ty5sbKyonTp0gwYMIC9e/dy8uRJGjZsyKRJk4iNjaVWrVrUqFGD0NBQVq5cSfXq1cmVKxcFCxYEYMuWLYmKtr///ptixYqxZcsWIiIiMJvNljfBXL582TLOxcWFVq1aMWrUKEtbVFSUHhQuIiIiyaLTxP9R1apVyZcvH5999hl//fUXCQkJxMbGsn79es6fP0+9evWoWbMmly5d4ueff8bT0xMnJycqVarE/Pnz8fb2BqBgwYLUq1ePL774gujoaBISEvj666/x8/Pjn3/+wcvLC8MwCAwMJD4+nj/++IPVq1db4vDz82Pjxo3s2bMHs9nM+fPn6dixIwsXLkyv1IiIiMgLRMXgf+To6Mjy5ctxdnamZ8+eeHh4UK9ePdavX8+iRYsoVaoU9vb21KlTB1tbW8qWLQtArVq1uHv3Lg0bNrTMNXHiRHLmzEnLli2pUaMGv/76K/Pnz8fZ2ZmcOXOyYMEC9u/fT7Vq1Rg6dCg+Pj6WbStVqsSUKVOYMmUKVatWpWPHjnh5eTFgwIDnnhMRERF58VgZOp8oT2AymQgJCcHNzQ0bG5v0DifdKA/KASgHoByAcgDKAWT8HKQkPq0MioiIiGRhKgZFREREsjAVg5IsJj2KRkREJFNSMSjJo0tLRUREMiUVgyIiIiJZmIpBSRYra6unDxIREZEXjopBSRZrK/1VERERyYz0DS8iIiKShakYFBEREcnCbNM7gMzq3LlzzJ49m/379xMTE0O+fPl488036dmzJzly5Ejv8EREREQArQymiaNHj9KqVSuKFCnC2rVrOXbsGPPmzeO3336jc+fOmEym9A5RREREBFAxmCYCAgJo2bIlffv2JW/evAC88sorTJ06lXz58jF79mx8fHwSbbNgwQI6dOgAgKurK/Pnz6devXq4u7vTvXt3IiMjAQgKCsLX15fOnTvj4eHBhg0b8Pf3JzAw0DJXeHg4rq6uhIeHA7B8+XIaNmyIh4cHzZo1Y/Xq1c8jDSIiIvICUDGYykJDQzlz5gxNmzZN0pc/f35mzZpFmzZtCAsL47fffrP0rV27Fl9f30Sfly5dyq5du7C2tqZ///6WvhMnTtCsWTP27duHt7f3E+MJCwtj3LhxzJ07l8OHDzNw4EBGjx5NVFRUKhytiIiIvOhUDKay69evA/cLv8cpUKAAtWvXZt26dcD94i48PJw333zTMqZfv34UK1aMl156iYEDB3LkyBHLSp+dnR0tWrTA3t4eR0fHJ8ZjY2ODYRisWLGCI0eOULNmTUJCQihQoMCzHqqIiIhkAioGU5mzszMAV65ceWT/1atXAfD19WXz5s0kJCTwww8/8Oabbya6saREiRKWnwsXLpxoTmdnZ6ytk/erK1y4MEuXLiUiIoIePXpQrVo1xo4dy927d1N+cCIiIpLpqBhMZUWKFKFs2bJs3rw5Sd+1a9eoX78+GzduxMvLC4C9e/eyZcsWWrdunWjsg2sEAcuK4IOi0Moq8dtArK2tSUhIsHyOjo5OtE+TycTMmTMJDg5m7ty5bN68mTVr1jzjkYqIiEhmoGIwDXz++ed8//33zJgxg+joaAzD4M8//6RHjx6UL18eHx8f7OzsaN68OdOmTcPJyQkPD49Ec8ycOZMrV67wzz//MGHCBGrXrk3BggUfub9SpUqxe/du/vnnH2JiYpg3b56l7+LFi3Tu3Jn9+/djbW1tmSNPnjxplwARERF5YagYTAPVqlXj22+/5eTJkzRp0oTKlSvTt29fatSowfz587GzswPunyo+efJkohtHHihfvjzt27fHy8uLnDlz8uWXXz52f927dydfvnw0aNCAFi1aWFYdASpUqEBAQAAjRozA3d2dDh060L59e956663UP3ARERF54eih02mkYsWKzJo164ljXFxcsLe3p2XLlkn6GjVqxOeff56k3dfXN0nxWLBgQebPn5+o7eE5/fz88PPzS37wIiIikmWoGEwH8fHxXLhwgSVLllC3bt3Hnv4VERERSWsqBtNBfHw8b7/9NoUKFWL27NnpHU6ymA0zNtikdxgiIiKSylQMpgMnJyeOHDny2P6//vrrOUaTPIbZSO8QREREJA3oBhIRERGRLEzFoIiIiEgWpmJQksXK2urpg0REROSFo2JQksXaSn9VREREMiN9w4uIiIhkYbqbOBV5eXlx5coVbG0Tp9Xd3R1ra2s8PDzo0aNHsubp3bv3I99M8qwGDx4MwPjx41N9bhEREXnxqBhMZSNHjkyTIk5EREQkLagYfE78/f2pVq0affr0YfDgwdjb2xMVFUVwcDB58+blvffe4913302yXWRkJOPGjeP333/n2rVr5M+fn549e1peL+fq6sqwYcP49ttviYqKwtXVlZEjR+Lq6grA9u3bmTJlChEREVSvXh2APHnyPL8DFxERkQxN1wymk6CgIPz9/Tl06BBdu3Zl/PjxREZGJhk3bNgw7Ozs2LRpE0ePHqVjx46MHj2aW7duWcZs2rSJb7/9ll27dpEtWzYmTpwIwP/+9z/69etH9+7dOXz4MG3atGH37t3P7RhFREQk41MxmMpGjhyJh4dHoj+3b99OMq569ep4enpia2tL69atMZlMhIaGJhk3ZswYhg8fjp2dHRcvXiRHjhzExcVx8+ZNyxh/f3+cnZ156aWXeOuttzh//jwAmzdv5vXXX6d58+bY2trSsGFD6tevn2bHLiIiIi8enSZOZcOHD0/WNYPOzs6Wn+3s7AAwm81JxoWFhTFx4kTOnz/Pyy+/TIkSJZKMzZ8/v+VnW1tbDOP+q+MiIyMpXLhwovmKFy9OdHR0Co5IREREMjOtDGZgCQkJdO/enRYtWhAcHMyqVat47733kr29i4sLYWFhidouX76c2mGKiIjIC0zFYAaWkJBAXFwcjo6OWFlZcfHiRSZNmmTpe5rmzZtz+vRpVq1axb1799izZw8///xzWoctIiIiLxAVgxlY9uzZGTt2LDNnzsTd3Z13330XT09P8ufPz+nTp5+6fbFixZg9ezbLli2jSpUqzJo1C29v7+cQuYiIiLworIwHF5iJPILJZCIkJAQ3NzdsbGzSO5x0ozwoB6AcgHIAygEoB5Dxc5CS+LQyKCIiIpKFqRgUERERycJUDEqymI2kj70RERGRF5+KQUkWw6xLS0VERDIjFYMiIiIiWZiKQUkWK2ur9A5BRERE0oCKQUkWayv9VREREcmM9A0vIiIikoWpGHwBxcTEcP369fQOQ0RERDIBFYNpbNmyZbi6urJ48eJUm9Pb25szZ848tv+DDz5g9uzZqbY/ERERybxs0zuAzG7ZsmW88847LFmyhI4dO2Jr++wpj46OfmL//Pnzn3kfIiIikjVoZTAN7d+/n2vXrjF48GDMZjNbt2619Hl5eREUFGT5HBwcjKurq+VzYGAgdevWpVq1arRu3Zrt27cD4OPjA0DXrl2ZN28eQUFB+Pr60rlzZzw8PNiwYQP+/v4EBgYCEBsby7Bhw2jUqBFubm7Url1bq4YiIiJioWIwDS1dupS2bdvi6OhI+/btWbhwYbK2O3DgACtXrmT16tUEBwfTpk0bhg4dSkJCgqWgnDdvHl27dgXgxIkTNGvWjH379uHt7Z1ori+//JLw8HDWrFnDsWPHGDZsGFOnTuXChQupe7AiIiLyQlIxmEYiIiLYvXs3HTp0AKBt27b8/fffHDx48KnbOjg4cPPmTVatWsXJkydp06YN+/fvx87O7pHj7ezsaNGiBfb29jg6Oibq69OnD1999RVOTk5cvnwZBwcHAKKiop7xCEVERCQz0DWDaWT58uXcu3ePFi1aWNru3bvHwoULqVat2hO3dXd3JzAwkKVLlzJ//nwcHR3x9/enZ8+eWFsnrd+dnZ0f2Q5w7do1vvjiC06ePEnRokV5/fXXATCb9a5hERERUTGYJu7evcuaNWv44osveOONNyztp0+fplu3bpw9exZra2sSEhIsfQ/fFHLx4kXy5cvHggULiI+PZ//+/fTu3Zvy5ctTr169JPuzsnr820H69euHl5cXCxYswNbWlujoaFatWpU6ByoiIiIvPJ0mTgMbNmzAysqKZs2a4eLiYvlTp04dypYty+LFiylVqhTbt28nLi6OK1eusGTJEsv2x48f54MPPuDUqVPY29uTL18+APLkyQOAvb09MTExyYolJiYGR0dHbGxsuH79OmPGjAFIVIiKiIhI1qViMA0sX76cZs2aPfIav3bt2rFu3Tq6dOnCrVu38PT05N1336V58+aWMT4+PnTu3JmePXvi5uZGv379GDJkCJUqVbLMMWDAAKZOnfrUWMaNG8fmzZupXLkyvr6+FCxYkHLlynH69OnUO2ARERF5YVkZhmGkdxCScZlMJkJCQnBzc8PGxia9w0k3yoNyAMoBKAegHIByABk/BymJTyuDIiIiIlmYikFJFrOhu49FREQyIxWDkiyGWVcTiIiIZEYqBkVERESyMBWDIiIiIlmYikFJFiv9TREREcmU9BUvyWJtlfFumxcREZFnp2JQREREJAvTu4mfwsvLiytXrmBrez9VhmFQokQJOnbsSJs2bdI5uqcbPHgwAOPHj0/nSERERCQjUjGYDCNHjsTX1xeA+Ph4du7cyWeffUZ0dDTdunVL5+hERERE/judJk4he3t7GjVqxKBBg5gxYwaxsbFcvXqVTz75BE9PT2rVqkVAQACxsbEABAcH4+Xlxddff03t2rWpVq0affr0sfQHBgbSr18/Bg0aROXKlalTpw5btmxh5syZvPHGG1SrVo1Zs2ZZ9n/06FHeffddatWqRYUKFfD19SUkJMSyr7p16zJgwAA8PDyYO3duotgjIiJo0KABY8eORW8hFBEREVAx+J/Vq1ePu3fvcuTIEXr16oW1tTVbt25lw4YNREVFERAQYBkbERFBZGQkP//8M6tXr+bYsWMsX77c0r9161bq16/PkSNHaN68OQMGDCA2NpZff/2VsWPHMm3aNCIiIoiLi6Nnz574+Piwa9cugoODKV68OBMnTrTMdfnyZUqWLMn+/ftp3769pT0sLAx/f39atGjBkCFDsLKyej6JEhERkQxNxeB/lCdPHgCOHz/OiRMnGD58OE5OTuTJk4dBgwaxadMmoqOjLeM//PBDHB0dKVGiBNWrV+fcuXOWvtKlS/Pmm29iZWWFp6cnJpOJHj16YGdnh5eXFwAXL17Ezs6OlStX0r59e+Lj44mIiCB37txERkYmis3Pzw87OzucnJyA+8Wov78/derUoW/fvmmdGhEREXmB6JrB/+j69esAlCxZEpPJRN26dRP129vbExYWZvns7Oxs+dnOzi7RadrcuXNbfra2vl+f58qVK9Fns9mMjY0NwcHBdO3aldu3b1O6dGlsbW2TnPItUKBAos+HDx/G09OT7du3079/f8vcIiIiIioG/6MdO3aQPXt28ufPj6OjI8HBwdjY3H8WX3x8PGFhYZQoUYIjR448da7knrL97bffGD16NCtWrOD1118HYOHChYlWGR81X+PGjZk4cSLvvPMOI0eOZMqUKcnan4iIiGR+Ok2cQvHx8WzevJkpU6bQv39/KleuTIkSJRg/fjy3bt0iLi6OsWPH0qlTJ0wmU6ruOyYmBmtraxwdHQEICQlhyZIlxMfHP3E7Ozs7bGxsGDduHNu2bWPz5s2pGpeIiIi8uLQymAzDhw9n9OjRADg4OFCyZElGjhxJ48aNAZgzZw4TJkygUaNG3L17l4oVK7Jo0SIcHBxSNQ5PT0/at29Phw4dMJvNFC1aFH9/fyZPnszVq1efun2pUqXo06cPI0eOpEqVKhQsWDBV4xMREZEXj5WhZ4zIE5hMJkJCQnBzc7OcBs+KlAflAJQDUA5AOQDlADJ+DlISn04Ti4iIiGRhKgZFREREsjAVg5IsZiN1b4YRERGRjEHFoCSLYU7vCERERCQtqBgUERERycJUDEqyWOtvioiISKakr3hJFiurjHfbvIiIiDw7FYMiIiIiWZiKQREREZEsTK+jSwMXL15kzpw57N69m+vXr2Nvb0+FChXo3Lkznp6e6R2eiIiIiIVWBlPZ6dOnad68OfHx8cybN48jR47w008/0bx5cz788EN+/fXX9A5RRERExELFYCoLCAjA09OTcePGUapUKWxsbMidOzctWrRg+PDhJCQkEBQUhK+vL507d8bDw4MNGzYQHx/PtGnTaNCgAdWqVaNr165cuHDBMu/Vq1f55JNP8PT0pFatWgQEBBAbG2vp37t3L35+fri7u+Pl5cW3335r6du3bx9+fn54eHjQpEkT1q9f/1xzIiIiIhmXisFUdPnyZY4dO8bbb7/9yP5WrVrRsGFDAE6cOEGzZs3Yt28f3t7eTJ06lZ07d7J48WJ2795NpUqV6Ny5M3fv3sVsNtOrVy+sra3ZunUrGzZsICoqioCAAADOnTtHjx49ePvttzl06BDTp09nypQp7N69m1OnTtGzZ0+6detGcHAwo0ePZuzYsezevfu55UVEREQyLhWDqejy5csAuLi4WNr279+Ph4cHHh4euLu74+PjA4CdnR0tWrTA3t4eBwcHVqxYwccff0yxYsVwcHDgww8/JCEhgZ07d/LHH39w4sQJhg8fjpOTE3ny5GHQoEFs2rSJ6OhoNm3aRPny5fHz88PW1pbXX3+d5cuXU758eVasWEGDBg1o1KgRNjY2VK5cmbZt27Js2bJ0yZGIiIhkLLqBJBU5OzsDEBkZySuvvAJAzZo1OXz4MABBQUHMmDHDMtb6/z/J+fr169y+fZt+/fpZ2gASEhKIiIjAZDJhMpmoW7duov3Z29sTFhZGVFQUhQsXTtT36quvAhAREcGBAwfw8PCw9JlMJooXL56ahy4iIiIvKBWDqahIkSJUqFCB1atXU6NGjSeOtbKysvycJ08eHBwcWLhwIW5ubpb2//3vfxQsWJC//voLR0dHgoODsbG5//Dn+Ph4wsLCKFGiBIUKFUpyY8r3339Pvnz5cHFxoVWrVowaNcrSFxUVhWEYqXDEIiIi8qLTaeJU9uB6vM8//5xz585hGAaxsbGsXbuWwMBAChQokGQba2tr/Pz8mDx5MpcvX8ZsNvPDDz/QtGlTLly4QMWKFSlRogTjx4/n1q1bxMXFMXbsWDp16oTJZKJJkyacPHmStWvXYjKZ+OOPPxg/fjy2trb4+fmxceNG9uzZg9ls5vz583Ts2JGFCxemQ3ZEREQko9HKYCorW7YsGzduZN68efTo0YMrV65gZWWFq6srH3zwAW3atGHjxo1Jths0aBCBgYG0b9+eGzduUKxYMaZPn065cuUAmDNnDhMmTKBRo0bcvXuXihUrsmjRIhwcHChevDhz585l8uTJjB49mnz58jF48GBq1aoFwJQpU5gyZQr9+vUjW7ZsNG3alI8//vi55kVEREQyJitD5wvlCUwmEyEhIbi5uVlOUWdFyoNyAMoBKAegHIByABk/BymJT6eJRURERLIwFYOSLIZhSu8QREREJA2oGJRkMZvTOwIRERFJCyoGRURERLIwFYMiIiIiWZiKQUkWa/1NERERyZT0FS/JYmWV8W6bFxERkWenYlBEREQkC8v0xaCXlxdBQUFJ2oOCgvDy8kqHiJ7u8OHDuLu7Wz5v3ryZmjVrUqVKFdauXYu7uzsXL178T3MPHjyYwYMHp1aoIiIi8oLT6+gyIA8PD44dO2b5vHr1apo0acKwYcMAaNmyZTpFJiIiIplNpl8ZfJrw8HBcXV1ZvXo1Xl5eVKlShffff5/Lly8DEBsbS//+/alevTqenp506dKFs2fPAhAYGEivXr3o06cPbm5ueHl5sXLlSsvcsbGxjBo1irp161KzZk369+/P1atXLf0nTpzA398fd3d3atWqxbRp0zAMg+DgYFxdXQHw8/PjwIEDrFixgoYNG1riDQ8PB+Dq1at88skneHp6UqtWLQICAoiNjbXsY/v27TRp0gQ3Nze6d+9OdHR0mudUREREXhxZvhh8YOfOnaxdu5atW7dy9epVZs2aBcDChQuJjY3l119/5ZdffsHZ2Zkvv/zSst327dupXLkyhw4dYtSoUYwePZr9+/cDMGTIEC5cuEBQUBDbtm3DycmJ3r17YxgGN27coHPnzlSvXp3g4GCWL19OUFBQomISYM2aNXh4eNC9e3e2bduWqM9sNtOrVy+sra3ZunUrGzZsICoqioCAAAD+97//0a9fP7p3787hw4dp06YNu3fvTss0ioiIyAtGp4n/v65du5IzZ07g/nWGD07TOjo6curUKdauXYunpydjx47F+qHnrLi6uvL+++8DUKtWLXx8fFi3bh1ly5Zl69atbNmyhXz58gH3i0MPDw9OnDjBmTNncHBw4MMPP8TKyorixYuzaNEismfPzoULF5IV8x9//MGJEydYtGgROXLkAGDQoEG8+eabfP7552zevJnXX3+d5s2bA9CwYUPq16+fOgkTERGRTCHTF4P29vaYTEnfq2symbC3t7d8zp8/v+VnW1tbDMMA7heJ9vb2rFmzhlGjRlGsWDEGDBhAo0aNAHj55ZcTzVuoUCH+/PNPIiIiAGjbtm2ifhsbG8LDw7ly5QqFChXCysrK0leyZEmAZBeD4eHhmEwm6tatm+SYw8LCiIyMpHDhwon6ihcvrlPFIiIiYpHpi8FChQpZCrOHXbhwgSJFijx1+7/++gsvLy86depETEwMy5cvp3///hw4cACAyMjIROPDw8MpVKgQBQsWBGDLli04Oztb+v/++2+KFSvG1q1buXTpEoZhWArCbdu2ERsbS6FChZJ1bC4uLjg6OhIcHIyNzf3nAMbHxxMWFkaJEiVwcXFh586diba5fPkyDg4OyZpfREREMr9Mf81gixYt+O6779i7dy9ms5n4+Hh27drF6tWr8fX1fer2q1evZuDAgVy7dg0nJyecnJzInj27ZVUxJCSEdevWYTKZ+PXXX9m+fTutW7emYMGC1KtXjy+++ILo6GgSEhL4+uuv8fPz459//qFevXrcu3eP2bNnEx8fT2hoKGPHjuXu3bvJPraKFStSokQJxo8fz61bt4iLi2Ps2LF06tQJk8lE8+bNOX36NKtWreLevXvs2bOHn3/++T/nUkRERDKfTL8y2LJlSxISEvjyyy8JDQ3FbDbzyiuvMGTIEJo0aWK5K/dxPv74Y0aNGkWTJk24e/cuJUuWZNasWZbVtddee43t27czZswY8ufPz6RJkyzPCJw4cSKTJ0+mZcuWxMbGUqZMGebPn29ZKVywYAHjxo1j0aJFZMuWjQ4dOtCuXTuCg4OTdWy2trbMmTOHCRMm0KhRI+7evUvFihVZtGgRDg4OFCtWjNmzZzN+/Hi++OILypcvj7e39zNkU0RERDIbK+PBxXGSYoGBgRw8eJClS5emdyhpxmQyERISgpubm+VUdFakPCgHoByAcgDKASgHkPFzkJL4Mv1pYhERERF5PBWDkiyGkfSObBEREXnxZfprBtNSnz590juENPfgKoKEBBNWVlm3IHzweKJHPaYoq1AOlANQDkA5AOUAMn4OHsSVnKsBdc2gPFF8fDzHjx9P7zBERETkP6hQoUKi5yo/iopBeSKz2cy9e/ewtrZO9IBsERERybgMw8BsNmNra5vozWmPomJQREREJAvTDSQiIiIiWZiKQREREZEsTMWgiIiISBamYlBEREQkC1MxKCIiIpKFqRgUERERycJUDIqIiIhkYSoGRURERLIwFYPyWNeuXaNXr154eHhQvXp1vvjiC+7du5feYSXb9evX8fb2Jjg42NL222+/0aZNG9zd3fHy8mL16tWJtvnhhx/w9vbGzc0NX19fjh07ZukzmUxMmDCBN954A3d3d3r27ElUVJSl/2n5etq+U9OpU6d4//33qVatGp6engwcOJDr169nqRzs37+fNm3aULlyZTw9PRk9ejRxcXFZKgcPx+zv78/gwYOTHUdmycHmzZspV64c7u7ulj+ffvpplsrBjRs3GDhwINWrV6dq1ar06tXLEmtWycH69esT/R1wd3fn9ddf5/XXX89SeXgsQ+QxOnbsaAwYMMC4ffu2ERoaajRp0sSYN29eeoeVLIcPHzYaNmxolC1b1jhw4IBhGIZx48YNo1q1asa3335rJCQkGPv27TPc3d2N3377zTAMwzhw4IDh7u5uHD582IiPjzcWLVpkVK9e3bh9+7ZhGIYRGBhoNGvWzLh48aIRExNjfPTRR0bXrl0t+3xSvp6279R0584dw9PT05g2bZpx9+5d4/r160bXrl2N7t27Z5kcXLt2zahQoYLx/fffGyaTyYiMjDSaNm1qTJs2Lcvk4GFfffWV8eqrrxqDBg1KVhyZKQfjx483Bg8enKQ9K+WgY8eOxocffmjcvHnTiImJMXr37m1069YtS+Xg3y5fvmx4enoaa9euzdJ5eEDFoDzS+fPnjbJlyxqXL1+2tG3atMmoV69eOkaVPEFBQUa9evWMTZs2JSoGV61aZTRq1CjR2ICAAGPgwIGGYRjGgAEDjGHDhiXqf/PNN401a9YYhmEYderUMdavX2/pu3LliuHq6mqEhoY+NV9P23dqOnv2rNGlSxfj3r17lrZt27YZlStXzjI5MAzDiImJMQzDMMxms/HXX38Z3t7extKlS7NUDgzDMPbt22c0btzY6Nu3r6UYzEo56NChg/Htt98mac8qOTh+/LhRoUIFy38PhmEY0dHRxunTp7NMDv7NbDYb/v7+xtChQ5MVS2bNw8N0mlge6cyZM+TOnZuCBQta2kqVKsXFixf5559/0jGyp6tVqxY///wzjRs3TtR+5swZypYtm6itdOnSnDp1CoC///77sf0xMTFcvnw5UX/+/PnJlSsXf/3111Pz9bR9p6aSJUsyf/58bGxsLG1bt26lfPnyWSYHAE5OTgDUrVuXZs2a4ezsjK+vb5bKwbVr1xg6dCiTJ08mW7ZslvaskgOz2cyJEyfYuXMn9evXp06dOnz++efcvHkzy+Tg999/p3Tp0qxatQpvb29q1arFhAkTcHZ2zjI5+Ld169bx999/Wy6byKp5eJiKQXmkW7duJfryACyfb9++nR4hJZuzszO2trZJ2h91TI6OjpbjeVL/rVu3AMiePXuS/lu3bj01X0/bd1oxDIOpU6fyyy+/MHTo0CyZg59++oldu3ZhbW1N3759s0wOzGYzn376Ke+//z6vvvpqor6skoPr169Trlw5fHx82Lx5MytWrOD8+fN8+umnWSYHN2/e5K+//uL8+fP88MMPrF27lsjISAYNGpRlcvAws9nM119/TY8ePSz/YMyKefg3FYPySNmzZ+fOnTuJ2h58zpEjR3qE9MyyZctmuYHggbi4OMvxPKn/wX+s/87Jg/6n5etp+04LsbGx9O3blw0bNvDtt9/i6uqa5XIA9/+PtWDBgnz66afs3r07y+Rgzpw52Nvb4+/vn6Qvq+Qgf/78LFu2DD8/P7Jly0bhwoX59NNP2bVrF4ZhZIkc2NvbAzB06FCcnJzInz8/H330Eb/++muWycHDgoODiYqKws/Pz9KWVf57eBIVg/JIZcqU4caNG1y9etXSdvbsWVxcXHjppZfSMbL/rmzZspw5cyZR299//02ZMmWA+8f8uP5cuXJRsGBB/v77b0vflStXuHHjBmXLln1qvp6279QWGhpK69atiY2NZc2aNbi6ugJZJwdHjx7lzTffJD4+3tIWHx+PnZ0dpUuXzhI5WLduHQcPHsTDwwMPDw82btzIxo0b8fDwyDJ/D06dOsWXX36JYRiWtvj4eKytralYsWKWyEHp0qUxm80kJCRY2sxmMwCvvfZalsjBw7Zu3Yq3t3eilbys8t/DEz3XKxTlhfLOO+8Y/fv3N2JiYix3QE2fPj29w0qRh28guX79uuHh4WEsWrTIiI+PN/bv32+4u7sb+/fvNwzDsNzFtX//fssdY1WrVjWio6MNwzCMqVOnGk2bNjVCQ0Mtd4x17NjRsq8n5etp+05NN27cMOrVq2cMHjzYMJlMifqySg5iY2ONunXrGmPHjjXu3r1rhIeHG35+fsbw4cOzTA7+bdCgQZYbSLJKDi5dumS4ubkZc+fONRISEoyIiAijbdu2xpAhQ7JMDuLj4w1vb2+jT58+RmxsrHHt2jXj3XffNT788MMsk4OHNW3a1Fi1alWitqyYh39TMSiPdeXKFaNPnz5GtWrVjBo1ahjjx49PdIfqi+DhYtAwDOP333832rVrZ7i7uxsNGjQwvv/++0Tj165da/j4+Bhubm6Gn5+fERISYumLj483Jk2aZNSuXduoXLmy0bNnT+Pq1auW/qfl62n7Ti0LFy40ypYta1SqVMlwc3NL9Cer5MAwDOPMmTPG+++/b3h4eBj169c3pkyZYty9ezdL5eBhDxeDyYkjs+QgODjYsq8aNWoYo0ePNuLi4rJUDi5fvmx89NFHhqenp+Hh4WEMHDjQuHnzZpbKwQNubm7Gzp07k7RntTz8m5VhPLR+LiIiIiJZiq4ZFBEREcnCVAyKiIiIZGEqBkVERESyMBWDIiIiIlmYikERERGRLEzFoIiIiEgWpmJQREREJAtTMSgikgWYTCbCwsJS3CcimZ+KQRGRNHbq1CkGDBhArVq1cHd3x9vbmwkTJnDjxo3nFkP//v1Zu3YtABcvXsTd3Z2LFy8m6UupS5cu8emnn/LGG2/g5uZG/fr1GT16NP/880+ytg8ODra8O/tRAgMD8ff3/0+xiUjyqBgUEUlDe/bs4Z133qFYsWJ8//33HD16lNmzZxMWFkbLli2JjIx8LnFER0dbfi5cuDDHjh2jcOHCSfpSwmw207lzZ3LlysWPP/5ISEgIS5Ys4fTp0/Tt2zdV4haRtKdiUEQkjdy7d48hQ4bQsWNHPvroIwoWLIiVlRWlSpVi+vTpuLi4MHbsWOD/tXd3IU23fxzH37k5lZZYoj2RHRQIPdFMVvag5UFLgswpFYknVijSQdADlqJpWBblSSBJhAoRViSFWSAGgpZF1iAkhgciqKGGrdjA3Hy4D8Jx79b/P+uPd/D38zoau36/a9/rOvpwfX/bZj4hy8/PJz8/HwCv18vVq1dJSUnBYrGQkJDApUuXmPpH0aysLG7cuEFmZiYWi4WUlBSePXsGQEFBAR0dHVRVVZGbm0tfXx+xsbH09fVNGysqKiI7OzugjtLSUs6dOzdtfS6Xi+7ubvbv3094eDgAq1atorCwkBUrVjA+Pg5Af38/p06dIiEhgR07dnD69GmGhoZm3LP379+Tnp7O5s2bOXLkCH19fb+7/SIySwqDIiJzxOFwMDg4SFpa2rSxoKAgMjIyePHiBWNjYz+dq7a2ltbWVmpra3E4HFRWVlJXV8fr16/91zx48ICCggLevHnD3r17KSoqYnR0lLKyMuLj48nJyeHWrVsB8/5zLCMjg/b2dv+JpdfrpbGxEbvdPq2myMhItm3bxsmTJykvL6e5uZnPnz8TGxvL5cuXMRgM+Hw+srOzMRgMNDU18fz5cwByc3OnrdvlcpGTk4PNZuPt27ecPXuW5ubmn2+0iPxPFAZFRObI1OnXVDv2n5YtW4bP5+PLly8/nevQoUPU1NQQFRXF0NAQ379/Z+HChQFtZpvNxrp16zCZTKSlpeF2uxkeHv6lmjdt2sSaNWt4+vQpAC0tLZjNZrZu3Trj9bdv3yYvLw+n08mZM2fYuXMnqamptLa2AtDR0UFvby8lJSUsWrSI8PBwSkpKcDqddHZ2BszV0tJCWFgYJ06cIDg4mC1btpCenv5L9YvIr1MYFBGZI9HR0cCPNulMhoaGCA4OJiIi4qdzjYyMUFRUhNVq5dixYzx+/JjJyUkmJib810RFRflfG41GgIDx2bLb7Tx58gSA+vp60tLSWLBgwYzXmkwmMjMzqamp4d27d9TX17NhwwZyc3Pp7u5meHiYxYsXYzab/feYzWYiIiKm7cvg4CDLly8P+KyYmJhfrl9Efo3CoIjIHLFYLCxdupSHDx/63/vw4QNNTU2Mj49TX19PUlISJpMJg8EA/GjLTvn7FzsKCwsJCwujra2NhoYGrly58ltBbzZSU1Pp7u7G4XDw8uXLGVvE8KMtnZiY6H820GAwsH79esrKyjCbzXR1dbFy5UpcLhcej8d/n9vtxuVyBYRX+HFS2t/fH7CugYGBOVihiPydwqCIyBwxGo2Ul5dTV1dHRUUFg4ODeL1erl+/TnJyMj09PZw/fx74cQJmNBppbGwE4NWrVwHPA3o8HkJCQggKCsLj8XDt2jU8Hg8+n29WtZhMJtxu96zGIiMjSUpKorS0lPj4+P/Y5t69ezejo6MUFxfT09PD+Pg4X79+pbq6GgCr1crGjRtZu3YtxcXFuN1u3G43Fy9eJCYmhri4uID5kpOTmZyc5ObNm3i9Xjo7OwOCtIjMDYVBEZE5tH37du7fv09vby92u53jx48zMTFBYmIioaGhVFZWMjw8THR0NBcuXKCyspK4uDju3r0bcCJXWFiI0+nEarWyb98+PB4Pu3btoqura1Z1HDx4kEePHnH06NFZjdntdj5+/Phfn9mLjo6mrq6OkZERsrKysFgs2Gw2HA4H9+7dY8mSJRiNRqqqqhgbG8Nms7Fnzx58Ph/V1dX+VvaU8PBw7ty5Q3t7O1arlYKCAmw226zWJyK/b8Hk1O8SiIjIv2pkZISGhgYOHDhAaGjony4ngNPpJCsri7a2NkJCQv50OSIyhxQGRUTEz+Px8OnTJyoqKli9erW/jS0i/7/UJhYREb+BgQEOHz7Mt2/fyMvL+9PliMi/QCeDIiIiIvOYTgZFRERE5jGFQREREZF5TGFQREREZB5TGBQRERGZxxQGRUREROYxhUERERGReUxhUERERGQeUxgUERERmcf+Au9ZGTM//y9eAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T09:27:25.277961Z",
     "start_time": "2025-11-21T09:27:25.061613Z"
    }
   },
   "cell_type": "code",
   "source": [
    "country=online.groupby(\"Country\")[\"TotaSales\"].sum().sort_values(ascending=False).head(10)\n",
    "country.plot(kind=\"bar\",color=\"cornflowerblue\")\n",
    "plt.title(\"Top 10 Most Sold Products\")\n",
    "plt.ylabel(\"Total Sales\")\n",
    "plt.show()"
   ],
   "id": "800456d7740e0aa2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T09:17:41.870978Z",
     "start_time": "2025-11-21T09:17:41.705603Z"
    }
   },
   "cell_type": "code",
   "source": [
    "online[\"Month\"]=online[\"InvoiceDate\"].dt.to_period(\"M\")\n",
    "mothsale=online.groupby(\"Month\")[(\"TotaSales\")].sum()\n",
    "mothsale.plot(marker=\"o\", color=\"cornflowerblue\")\n",
    "plt.title(\"monthly sales per month\")\n",
    "plt.ylabel(\"Total Sales\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ],
   "id": "3916e506f9e46c05",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T09:28:56.772357Z",
     "start_time": "2025-11-21T09:28:56.454927Z"
    }
   },
   "cell_type": "code",
   "source": [
    "online[\"Weekday\"]=online[\"InvoiceDate\"].dt.day_name()\n",
    "weekday=online.groupby(\"Weekday\")[\"TotaSales\"].sum()\n",
    "weekday.plot(kind=\"bar\",color=\"green\")\n",
    "plt.title(\"weekday\")\n",
    "plt.ylabel(\"Total Sales\")\n",
    "plt.title(\"weekday sales per weekday\")\n",
    "plt.ylabel(\"Total Sales\")\n",
    "plt.show()"
   ],
   "id": "5185ae5f9e557f00",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T09:37:07.400224Z",
     "start_time": "2025-11-21T09:37:05.973557Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "1790687fda6cd3e",
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'CustomerID'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[28], line 5\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# 数据预处理\u001B[39;00m\n\u001B[0;32m      2\u001B[0m \n\u001B[0;32m      3\u001B[0m \u001B[38;5;66;03m# 计算RFM指标\u001B[39;00m\n\u001B[0;32m      4\u001B[0m snapshot_date \u001B[38;5;241m=\u001B[39m online[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvoiceDate\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mmax() \u001B[38;5;241m+\u001B[39m pd\u001B[38;5;241m.\u001B[39mTimedelta(days\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m----> 5\u001B[0m rfm \u001B[38;5;241m=\u001B[39m online\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCustomerID\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39magg({\n\u001B[0;32m      6\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvoiceDate\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28;01mlambda\u001B[39;00m x: (snapshot_date \u001B[38;5;241m-\u001B[39m x\u001B[38;5;241m.\u001B[39mmax())\u001B[38;5;241m.\u001B[39mdays,\n\u001B[0;32m      7\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvoice\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnunique\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m      8\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTotalSales\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msum\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m      9\u001B[0m })\n\u001B[0;32m     11\u001B[0m \u001B[38;5;66;03m# 重命名列\u001B[39;00m\n\u001B[0;32m     12\u001B[0m rfm\u001B[38;5;241m.\u001B[39mrename(\n\u001B[0;32m     13\u001B[0m     columns\u001B[38;5;241m=\u001B[39m{\n\u001B[0;32m     14\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvoiceDate\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRecency\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     18\u001B[0m     inplace\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[0;32m     19\u001B[0m )\n",
      "File \u001B[1;32mE:\\anaconda\\Ancona\\Lib\\site-packages\\pandas\\core\\frame.py:9183\u001B[0m, in \u001B[0;36mgroupby\u001B[1;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001B[0m\n\u001B[0;32m   9064\u001B[0m             \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mloc[:, col] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m[col]\u001B[38;5;241m.\u001B[39mwhere(mask, that)\n\u001B[0;32m   9066\u001B[0m \u001B[38;5;66;03m# ----------------------------------------------------------------------\u001B[39;00m\n\u001B[0;32m   9067\u001B[0m \u001B[38;5;66;03m# Data reshaping\u001B[39;00m\n\u001B[0;32m   9068\u001B[0m \u001B[38;5;129m@Appender\u001B[39m(\n\u001B[0;32m   9069\u001B[0m     dedent(\n\u001B[0;32m   9070\u001B[0m \u001B[38;5;250m        \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m   9071\u001B[0m \u001B[38;5;124;03m    Examples\u001B[39;00m\n\u001B[0;32m   9072\u001B[0m \u001B[38;5;124;03m    --------\u001B[39;00m\n\u001B[0;32m   9073\u001B[0m \u001B[38;5;124;03m    >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\u001B[39;00m\n\u001B[0;32m   9074\u001B[0m \u001B[38;5;124;03m    ...                               'Parrot', 'Parrot'],\u001B[39;00m\n\u001B[0;32m   9075\u001B[0m \u001B[38;5;124;03m    ...                    'Max Speed': [380., 370., 24., 26.]})\u001B[39;00m\n\u001B[0;32m   9076\u001B[0m \u001B[38;5;124;03m    >>> df\u001B[39;00m\n\u001B[0;32m   9077\u001B[0m \u001B[38;5;124;03m       Animal  Max Speed\u001B[39;00m\n\u001B[0;32m   9078\u001B[0m \u001B[38;5;124;03m    0  Falcon      380.0\u001B[39;00m\n\u001B[0;32m   9079\u001B[0m \u001B[38;5;124;03m    1  Falcon      370.0\u001B[39;00m\n\u001B[0;32m   9080\u001B[0m \u001B[38;5;124;03m    2  Parrot       24.0\u001B[39;00m\n\u001B[0;32m   9081\u001B[0m \u001B[38;5;124;03m    3  Parrot       26.0\u001B[39;00m\n\u001B[0;32m   9082\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(['Animal']).mean()\u001B[39;00m\n\u001B[0;32m   9083\u001B[0m \u001B[38;5;124;03m            Max Speed\u001B[39;00m\n\u001B[0;32m   9084\u001B[0m \u001B[38;5;124;03m    Animal\u001B[39;00m\n\u001B[0;32m   9085\u001B[0m \u001B[38;5;124;03m    Falcon      375.0\u001B[39;00m\n\u001B[0;32m   9086\u001B[0m \u001B[38;5;124;03m    Parrot       25.0\u001B[39;00m\n\u001B[0;32m   9087\u001B[0m \n\u001B[0;32m   9088\u001B[0m \u001B[38;5;124;03m    **Hierarchical Indexes**\u001B[39;00m\n\u001B[0;32m   9089\u001B[0m \n\u001B[0;32m   9090\u001B[0m \u001B[38;5;124;03m    We can groupby different levels of a hierarchical index\u001B[39;00m\n\u001B[0;32m   9091\u001B[0m \u001B[38;5;124;03m    using the `level` parameter:\u001B[39;00m\n\u001B[0;32m   9092\u001B[0m \n\u001B[0;32m   9093\u001B[0m \u001B[38;5;124;03m    >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],\u001B[39;00m\n\u001B[0;32m   9094\u001B[0m \u001B[38;5;124;03m    ...           ['Captive', 'Wild', 'Captive', 'Wild']]\u001B[39;00m\n\u001B[0;32m   9095\u001B[0m \u001B[38;5;124;03m    >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))\u001B[39;00m\n\u001B[0;32m   9096\u001B[0m \u001B[38;5;124;03m    >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},\u001B[39;00m\n\u001B[0;32m   9097\u001B[0m \u001B[38;5;124;03m    ...                   index=index)\u001B[39;00m\n\u001B[0;32m   9098\u001B[0m \u001B[38;5;124;03m    >>> df\u001B[39;00m\n\u001B[0;32m   9099\u001B[0m \u001B[38;5;124;03m                    Max Speed\u001B[39;00m\n\u001B[0;32m   9100\u001B[0m \u001B[38;5;124;03m    Animal Type\u001B[39;00m\n\u001B[0;32m   9101\u001B[0m \u001B[38;5;124;03m    Falcon Captive      390.0\u001B[39;00m\n\u001B[0;32m   9102\u001B[0m \u001B[38;5;124;03m           Wild         350.0\u001B[39;00m\n\u001B[0;32m   9103\u001B[0m \u001B[38;5;124;03m    Parrot Captive       30.0\u001B[39;00m\n\u001B[0;32m   9104\u001B[0m \u001B[38;5;124;03m           Wild          20.0\u001B[39;00m\n\u001B[0;32m   9105\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(level=0).mean()\u001B[39;00m\n\u001B[0;32m   9106\u001B[0m \u001B[38;5;124;03m            Max Speed\u001B[39;00m\n\u001B[0;32m   9107\u001B[0m \u001B[38;5;124;03m    Animal\u001B[39;00m\n\u001B[0;32m   9108\u001B[0m \u001B[38;5;124;03m    Falcon      370.0\u001B[39;00m\n\u001B[0;32m   9109\u001B[0m \u001B[38;5;124;03m    Parrot       25.0\u001B[39;00m\n\u001B[0;32m   9110\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(level=\"Type\").mean()\u001B[39;00m\n\u001B[0;32m   9111\u001B[0m \u001B[38;5;124;03m             Max Speed\u001B[39;00m\n\u001B[0;32m   9112\u001B[0m \u001B[38;5;124;03m    Type\u001B[39;00m\n\u001B[0;32m   9113\u001B[0m \u001B[38;5;124;03m    Captive      210.0\u001B[39;00m\n\u001B[0;32m   9114\u001B[0m \u001B[38;5;124;03m    Wild         185.0\u001B[39;00m\n\u001B[0;32m   9115\u001B[0m \n\u001B[0;32m   9116\u001B[0m \u001B[38;5;124;03m    We can also choose to include NA in group keys or not by setting\u001B[39;00m\n\u001B[0;32m   9117\u001B[0m \u001B[38;5;124;03m    `dropna` parameter, the default setting is `True`.\u001B[39;00m\n\u001B[0;32m   9118\u001B[0m \n\u001B[0;32m   9119\u001B[0m \u001B[38;5;124;03m    >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]\u001B[39;00m\n\u001B[0;32m   9120\u001B[0m \u001B[38;5;124;03m    >>> df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\u001B[39;00m\n\u001B[0;32m   9121\u001B[0m \n\u001B[0;32m   9122\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(by=[\"b\"]).sum()\u001B[39;00m\n\u001B[0;32m   9123\u001B[0m \u001B[38;5;124;03m        a   c\u001B[39;00m\n\u001B[0;32m   9124\u001B[0m \u001B[38;5;124;03m    b\u001B[39;00m\n\u001B[0;32m   9125\u001B[0m \u001B[38;5;124;03m    1.0 2   3\u001B[39;00m\n\u001B[0;32m   9126\u001B[0m \u001B[38;5;124;03m    2.0 2   5\u001B[39;00m\n\u001B[0;32m   9127\u001B[0m \n\u001B[0;32m   9128\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(by=[\"b\"], dropna=False).sum()\u001B[39;00m\n\u001B[0;32m   9129\u001B[0m \u001B[38;5;124;03m        a   c\u001B[39;00m\n\u001B[0;32m   9130\u001B[0m \u001B[38;5;124;03m    b\u001B[39;00m\n\u001B[0;32m   9131\u001B[0m \u001B[38;5;124;03m    1.0 2   3\u001B[39;00m\n\u001B[0;32m   9132\u001B[0m \u001B[38;5;124;03m    2.0 2   5\u001B[39;00m\n\u001B[0;32m   9133\u001B[0m \u001B[38;5;124;03m    NaN 1   4\u001B[39;00m\n\u001B[0;32m   9134\u001B[0m \n\u001B[0;32m   9135\u001B[0m \u001B[38;5;124;03m    >>> l = [[\"a\", 12, 12], [None, 12.3, 33.], [\"b\", 12.3, 123], [\"a\", 1, 1]]\u001B[39;00m\n\u001B[0;32m   9136\u001B[0m \u001B[38;5;124;03m    >>> df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\u001B[39;00m\n\u001B[0;32m   9137\u001B[0m \n\u001B[0;32m   9138\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(by=\"a\").sum()\u001B[39;00m\n\u001B[0;32m   9139\u001B[0m \u001B[38;5;124;03m        b     c\u001B[39;00m\n\u001B[0;32m   9140\u001B[0m \u001B[38;5;124;03m    a\u001B[39;00m\n\u001B[0;32m   9141\u001B[0m \u001B[38;5;124;03m    a   13.0   13.0\u001B[39;00m\n\u001B[0;32m   9142\u001B[0m \u001B[38;5;124;03m    b   12.3  123.0\u001B[39;00m\n\u001B[0;32m   9143\u001B[0m \n\u001B[0;32m   9144\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(by=\"a\", dropna=False).sum()\u001B[39;00m\n\u001B[0;32m   9145\u001B[0m \u001B[38;5;124;03m        b     c\u001B[39;00m\n\u001B[0;32m   9146\u001B[0m \u001B[38;5;124;03m    a\u001B[39;00m\n\u001B[0;32m   9147\u001B[0m \u001B[38;5;124;03m    a   13.0   13.0\u001B[39;00m\n\u001B[0;32m   9148\u001B[0m \u001B[38;5;124;03m    b   12.3  123.0\u001B[39;00m\n\u001B[0;32m   9149\u001B[0m \u001B[38;5;124;03m    NaN 12.3   33.0\u001B[39;00m\n\u001B[0;32m   9150\u001B[0m \n\u001B[0;32m   9151\u001B[0m \u001B[38;5;124;03m    When using ``.apply()``, use ``group_keys`` to include or exclude the\u001B[39;00m\n\u001B[0;32m   9152\u001B[0m \u001B[38;5;124;03m    group keys. The ``group_keys`` argument defaults to ``True`` (include).\u001B[39;00m\n\u001B[0;32m   9153\u001B[0m \n\u001B[0;32m   9154\u001B[0m \u001B[38;5;124;03m    >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\u001B[39;00m\n\u001B[0;32m   9155\u001B[0m \u001B[38;5;124;03m    ...                               'Parrot', 'Parrot'],\u001B[39;00m\n\u001B[0;32m   9156\u001B[0m \u001B[38;5;124;03m    ...                    'Max Speed': [380., 370., 24., 26.]})\u001B[39;00m\n\u001B[0;32m   9157\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(\"Animal\", group_keys=True)[['Max Speed']].apply(lambda x: x)\u001B[39;00m\n\u001B[0;32m   9158\u001B[0m \u001B[38;5;124;03m              Max Speed\u001B[39;00m\n\u001B[0;32m   9159\u001B[0m \u001B[38;5;124;03m    Animal\u001B[39;00m\n\u001B[0;32m   9160\u001B[0m \u001B[38;5;124;03m    Falcon 0      380.0\u001B[39;00m\n\u001B[0;32m   9161\u001B[0m \u001B[38;5;124;03m           1      370.0\u001B[39;00m\n\u001B[0;32m   9162\u001B[0m \u001B[38;5;124;03m    Parrot 2       24.0\u001B[39;00m\n\u001B[0;32m   9163\u001B[0m \u001B[38;5;124;03m           3       26.0\u001B[39;00m\n\u001B[0;32m   9164\u001B[0m \n\u001B[0;32m   9165\u001B[0m \u001B[38;5;124;03m    >>> df.groupby(\"Animal\", group_keys=False)[['Max Speed']].apply(lambda x: x)\u001B[39;00m\n\u001B[0;32m   9166\u001B[0m \u001B[38;5;124;03m       Max Speed\u001B[39;00m\n\u001B[0;32m   9167\u001B[0m \u001B[38;5;124;03m    0      380.0\u001B[39;00m\n\u001B[0;32m   9168\u001B[0m \u001B[38;5;124;03m    1      370.0\u001B[39;00m\n\u001B[0;32m   9169\u001B[0m \u001B[38;5;124;03m    2       24.0\u001B[39;00m\n\u001B[0;32m   9170\u001B[0m \u001B[38;5;124;03m    3       26.0\u001B[39;00m\n\u001B[0;32m   9171\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m   9172\u001B[0m     )\n\u001B[0;32m   9173\u001B[0m )\n\u001B[0;32m   9174\u001B[0m \u001B[38;5;129m@Appender\u001B[39m(_shared_docs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgroupby\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m%\u001B[39m _shared_doc_kwargs)\n\u001B[0;32m   9175\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mgroupby\u001B[39m(\n\u001B[0;32m   9176\u001B[0m     \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m   9177\u001B[0m     by\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m   9178\u001B[0m     axis: Axis \u001B[38;5;241m|\u001B[39m lib\u001B[38;5;241m.\u001B[39mNoDefault \u001B[38;5;241m=\u001B[39m lib\u001B[38;5;241m.\u001B[39mno_default,\n\u001B[0;32m   9179\u001B[0m     level: IndexLabel \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m   9180\u001B[0m     as_index: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m   9181\u001B[0m     sort: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m   9182\u001B[0m     group_keys: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[1;32m-> 9183\u001B[0m     observed: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m|\u001B[39m lib\u001B[38;5;241m.\u001B[39mNoDefault \u001B[38;5;241m=\u001B[39m lib\u001B[38;5;241m.\u001B[39mno_default,\n\u001B[0;32m   9184\u001B[0m     dropna: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m   9185\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m DataFrameGroupBy:\n\u001B[0;32m   9186\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m axis \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mno_default:\n\u001B[0;32m   9187\u001B[0m         axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_axis_number(axis)\n",
      "File \u001B[1;32mE:\\anaconda\\Ancona\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1329\u001B[0m, in \u001B[0;36m__init__\u001B[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001B[0m\n\u001B[0;32m      0\u001B[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
      "File \u001B[1;32mE:\\anaconda\\Ancona\\Lib\\site-packages\\pandas\\core\\groupby\\grouper.py:1043\u001B[0m, in \u001B[0;36mget_grouper\u001B[1;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001B[0m\n\u001B[0;32m   1041\u001B[0m         in_axis, level, gpr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m, gpr, \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m   1042\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1043\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(gpr)\n\u001B[0;32m   1044\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(gpr, Grouper) \u001B[38;5;129;01mand\u001B[39;00m gpr\u001B[38;5;241m.\u001B[39mkey \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m   1045\u001B[0m     \u001B[38;5;66;03m# Add key to exclusions\u001B[39;00m\n\u001B[0;32m   1046\u001B[0m     exclusions\u001B[38;5;241m.\u001B[39madd(gpr\u001B[38;5;241m.\u001B[39mkey)\n",
      "\u001B[1;31mKeyError\u001B[0m: 'CustomerID'"
     ]
    }
   ],
   "execution_count": 28
  }
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